{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Anti-de Sitter spacetime\n", "\n", "This notebook demonstrates some capabilities of SageMath in computations regarding the 4-dimensional anti-de Sitter spacetime. The corresponding tools have been developed within the [SageManifolds](https://sagemanifolds.obspm.fr) project." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*NB:* a version of SageMath at least equal to 8.8 is required to run this notebook:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'SageMath version 9.4, Release Date: 2021-08-22'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "version()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we set up the notebook to display mathematical objects using LaTeX rendering:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%display latex" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We initialize a time counter for benchmarking:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import time\n", "comput_time0 = time.perf_counter()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Spacetime manifold\n", "\n", "We declare the anti-de Sitter spacetime as a 4-dimensional Lorentzian manifold:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4-dimensional Lorentzian manifold M\n" ] }, { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\mathcal{M}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\mathcal{M}$$" ], "text/plain": [ "4-dimensional Lorentzian manifold M" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "M = Manifold(4, 'M', r'\\mathcal{M}', structure='Lorentzian')\n", "print(M); M" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

We consider hyperbolic coordinates $(\\tau,\\rho,\\theta,\\phi)$ on $\\mathcal{M}$. Allowing for the standard coordinate singularities at $\\rho=0$, $\\theta=0$ or $\\theta=\\pi$, these coordinates cover the entire spacetime manifold (which is topologically $\\mathbb{R}^4$). If we restrict ourselves to regular coordinates (i.e. to considering only mathematically well defined charts), the hyperbolic coordinates cover only an open part of $\\mathcal{M}$, which we call $\\mathcal{M}_0$, on which $\\rho$ spans the open interval $(0,+\\infty)$, $\\theta$ the open interval $(0,\\pi)$ and $\\phi$ the open interval $(0,2\\pi)$. Therefore, we declare:

" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Chart (M_0, (ta, rh, th, ph))\n" ] }, { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left(\\mathcal{M}_0,({\\tau}, {\\rho}, {\\theta}, {\\phi})\\right)\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left(\\mathcal{M}_0,({\\tau}, {\\rho}, {\\theta}, {\\phi})\\right)$$" ], "text/plain": [ "Chart (M_0, (ta, rh, th, ph))" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "M0 = M.open_subset('M_0', r'\\mathcal{M}_0' )\n", "X_hyp. = M0.chart(r'ta:\\tau rh:(0,+oo):\\rho th:(0,pi):\\theta ph:(0,2*pi):\\phi')\n", "print(X_hyp); X_hyp" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}{\\tau} :\\ \\left( -\\infty, +\\infty \\right) ;\\quad {\\rho} :\\ \\left( 0 , +\\infty \\right) ;\\quad {\\theta} :\\ \\left( 0 , \\pi \\right) ;\\quad {\\phi} :\\ \\left( 0 , 2 \\, \\pi \\right)\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}{\\tau} :\\ \\left( -\\infty, +\\infty \\right) ;\\quad {\\rho} :\\ \\left( 0 , +\\infty \\right) ;\\quad {\\theta} :\\ \\left( 0 , \\pi \\right) ;\\quad {\\phi} :\\ \\left( 0 , 2 \\, \\pi \\right)$$" ], "text/plain": [ "ta: (-oo, +oo); rh: (0, +oo); th: (0, pi); ph: (0, 2*pi)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_hyp.coord_range()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## $\\mathbb{R}^{2,3}$ as an ambient space\n", "The AdS metric can be defined as that induced by the immersion of $\\mathcal{M}$ in $\\mathbb{R}^{2,3}$, the latter being nothing but $\\mathbb{R}^5$ equipped with a flat pseudo-Riemannian metric of signature $(-,-,+,+,+)$. Let us construct $\\mathbb{R}^{2,3}$ as a 5-dimensional manifold covered by canonical coordinates:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Chart (R23, (U, V, X, Y, Z))\n" ] }, { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left(\\mathbb{R}^{2,3},(U, V, X, Y, Z)\\right)\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left(\\mathbb{R}^{2,3},(U, V, X, Y, Z)\\right)$$" ], "text/plain": [ "Chart (R23, (U, V, X, Y, Z))" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "R23 = Manifold(5, 'R23', r'\\mathbb{R}^{2,3}', structure='pseudo-Riemannian', signature=1, \n", " metric_name='h')\n", "X23. = R23.chart()\n", "print(X23); X23" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We define the pseudo-Riemannian metric of $\\mathbb{R}^{2,3}$:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}h = -\\mathrm{d} U\\otimes \\mathrm{d} U-\\mathrm{d} V\\otimes \\mathrm{d} V+\\mathrm{d} X\\otimes \\mathrm{d} X+\\mathrm{d} Y\\otimes \\mathrm{d} Y+\\mathrm{d} Z\\otimes \\mathrm{d} Z\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}h = -\\mathrm{d} U\\otimes \\mathrm{d} U-\\mathrm{d} V\\otimes \\mathrm{d} V+\\mathrm{d} X\\otimes \\mathrm{d} X+\\mathrm{d} Y\\otimes \\mathrm{d} Y+\\mathrm{d} Z\\otimes \\mathrm{d} Z$$" ], "text/plain": [ "h = -dU⊗dU - dV⊗dV + dX⊗dX + dY⊗dY + dZ⊗dZ" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "h = R23.metric()\n", "h[0,0], h[1,1], h[2,2], h[3,3], h[4,4] = -1, -1, 1, 1, 1\n", "h.display()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The AdS immersion into $\\mathbb{R}^{2,3}$ is defined as a differential map $\\Phi$ from $\\mathcal{M}$ to $\\mathbb{R}^{2,3}$, by providing its expression in terms of $\\mathcal{M}$'s default chart (which is X_hyp = $(\\mathcal{M}_0,(\\tau,\\rho,\\theta,\\phi))$ ) and $\\mathbb{R}^{2,3}$'s default chart (which is X23 = $(\\mathbb{R}^{2,3},(U,V,X,Y,Z))$ ):" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Differentiable map Phi from the 4-dimensional Lorentzian manifold M to the 5-dimensional pseudo-Riemannian manifold R23\n" ] }, { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} \\Phi:& \\mathcal{M} & \\longrightarrow & \\mathbb{R}^{2,3} \\\\ \\mbox{on}\\ \\mathcal{M}_0 : & \\left({\\tau}, {\\rho}, {\\theta}, {\\phi}\\right) & \\longmapsto & \\left(U, V, X, Y, Z\\right) = \\left({\\ell} \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right) \\cosh\\left({\\rho}\\right), {\\ell} \\cosh\\left({\\rho}\\right) \\sin\\left(\\frac{{\\tau}}{{\\ell}}\\right), {\\ell} \\cos\\left({\\phi}\\right) \\sin\\left({\\theta}\\right) \\sinh\\left({\\rho}\\right), {\\ell} \\sin\\left({\\phi}\\right) \\sin\\left({\\theta}\\right) \\sinh\\left({\\rho}\\right), {\\ell} \\cos\\left({\\theta}\\right) \\sinh\\left({\\rho}\\right)\\right) \\end{array}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} \\Phi:& \\mathcal{M} & \\longrightarrow & \\mathbb{R}^{2,3} \\\\ \\mbox{on}\\ \\mathcal{M}_0 : & \\left({\\tau}, {\\rho}, {\\theta}, {\\phi}\\right) & \\longmapsto & \\left(U, V, X, Y, Z\\right) = \\left({\\ell} \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right) \\cosh\\left({\\rho}\\right), {\\ell} \\cosh\\left({\\rho}\\right) \\sin\\left(\\frac{{\\tau}}{{\\ell}}\\right), {\\ell} \\cos\\left({\\phi}\\right) \\sin\\left({\\theta}\\right) \\sinh\\left({\\rho}\\right), {\\ell} \\sin\\left({\\phi}\\right) \\sin\\left({\\theta}\\right) \\sinh\\left({\\rho}\\right), {\\ell} \\cos\\left({\\theta}\\right) \\sinh\\left({\\rho}\\right)\\right) \\end{array}$$" ], "text/plain": [ "Phi: M → R23\n", "on M_0: (ta, rh, th, ph) ↦ (U, V, X, Y, Z) = (l*cos(ta/l)*cosh(rh), l*cosh(rh)*sin(ta/l), l*cos(ph)*sin(th)*sinh(rh), l*sin(ph)*sin(th)*sinh(rh), l*cos(th)*sinh(rh))" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "var('l', latex_name=r'\\ell', domain='real')\n", "assume(l>0)\n", "Phi = M.diff_map(R23, [l*cosh(rh)*cos(ta/l),\n", " l*cosh(rh)*sin(ta/l),\n", " l*sinh(rh)*sin(th)*cos(ph),\n", " l*sinh(rh)*sin(th)*sin(ph),\n", " l*sinh(rh)*cos(th)],\n", " name='Phi', latex_name=r'\\Phi')\n", "print(Phi); Phi.display()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The constant $\\ell$ is the AdS length parameter. Considering AdS metric as a solution of vacuum Einstein equation with negative cosmological constant $\\Lambda$, one has $\\ell = \\sqrt{-3/\\Lambda}$.\n", "\n", "Let us evaluate the image of a point via the map $\\Phi$:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Point p on the 4-dimensional Lorentzian manifold M\n" ] } ], "source": [ "p = M((ta, rh, th, ph), name='p'); print(p)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The coordinates of $p$ in the chart `X_hyp`:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left({\\tau}, {\\rho}, {\\theta}, {\\phi}\\right)\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left({\\tau}, {\\rho}, {\\theta}, {\\phi}\\right)$$" ], "text/plain": [ "(ta, rh, th, ph)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_hyp(p)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Point Phi(p) on the 5-dimensional pseudo-Riemannian manifold R23\n" ] } ], "source": [ "q = Phi(p); print(q)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left({\\ell} \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right) \\cosh\\left({\\rho}\\right), {\\ell} \\cosh\\left({\\rho}\\right) \\sin\\left(\\frac{{\\tau}}{{\\ell}}\\right), {\\ell} \\cos\\left({\\phi}\\right) \\sin\\left({\\theta}\\right) \\sinh\\left({\\rho}\\right), {\\ell} \\sin\\left({\\phi}\\right) \\sin\\left({\\theta}\\right) \\sinh\\left({\\rho}\\right), {\\ell} \\cos\\left({\\theta}\\right) \\sinh\\left({\\rho}\\right)\\right)\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left({\\ell} \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right) \\cosh\\left({\\rho}\\right), {\\ell} \\cosh\\left({\\rho}\\right) \\sin\\left(\\frac{{\\tau}}{{\\ell}}\\right), {\\ell} \\cos\\left({\\phi}\\right) \\sin\\left({\\theta}\\right) \\sinh\\left({\\rho}\\right), {\\ell} \\sin\\left({\\phi}\\right) \\sin\\left({\\theta}\\right) \\sinh\\left({\\rho}\\right), {\\ell} \\cos\\left({\\theta}\\right) \\sinh\\left({\\rho}\\right)\\right)$$" ], "text/plain": [ "(l*cos(ta/l)*cosh(rh),\n", " l*cosh(rh)*sin(ta/l),\n", " l*cos(ph)*sin(th)*sinh(rh),\n", " l*sin(ph)*sin(th)*sinh(rh),\n", " l*cos(th)*sinh(rh))" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X23(q)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The image of $\\mathcal{M}$ by the immersion $\\Phi$ is a hyperboloid of one sheet, of equation $$-U^2-V^2+X^2+Y^2+Z^2=-\\ell^2.$$\n", "Indeed:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}-{\\ell}^{2}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}-{\\ell}^{2}$$" ], "text/plain": [ "-l^2" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(Uq,Vq,Xq,Yq,Zq) = X23(q)\n", "s = - Uq^2 - Vq^2 + Xq^2 + Yq^2 + Zq^2\n", "s.simplify_full()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We may use the immersion $\\Phi$ to draw the coordinate grid $(\\tau,\\rho)$ in terms of the coordinates $(U,V,X)$ for $\\theta=\\pi/2$ and $\\phi=0$ ($X\\geq 0$ part) or $\\phi=\\pi$ \n", "($X\\leq 0$ part). The red (rep. grey) curves are those for which $\\rho={\\rm const}$ \n", "(resp. $\\tau={\\rm const}$):" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "Graphics3d Object" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "graph_hyp = X_hyp.plot(X23, mapping=Phi, ambient_coords=(V,X,U), fixed_coords={th:pi/2, ph:0}, \n", " ranges={ta:(0,2*pi), rh:(0,2)}, number_values=9, \n", " color={ta:'red', rh:'grey'}, thickness=2, parameters={l:1}, \n", " label_axes=False) # phi = 0 => X > 0 part\n", "graph_hyp += X_hyp.plot(X23, mapping=Phi, ambient_coords=(V,X,U), fixed_coords={th:pi/2, ph:pi},\n", " ranges={ta:(0,2*pi), rh:(0,2)}, number_values=9, \n", " color={ta:'red', rh:'grey'}, thickness=2, parameters={l:1}, \n", " label_axes=False) # phi = pi => X < 0 part\n", "show(graph_hyp, aspect_ratio=1, axes_labels=['V','X','U'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To have a nicer picture, we add the plot of the hyperboloid obtained by `parametric_plot` with $(\\tau,\\rho)$ as parameters and the expressions of $(U,V,X)$ in terms of $(\\tau,\\rho)$ deduced from the coordinate representation of $\\Phi$:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left({\\ell} \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right) \\cosh\\left({\\rho}\\right), {\\ell} \\cosh\\left({\\rho}\\right) \\sin\\left(\\frac{{\\tau}}{{\\ell}}\\right), {\\ell} \\cos\\left({\\phi}\\right) \\sin\\left({\\theta}\\right) \\sinh\\left({\\rho}\\right), {\\ell} \\sin\\left({\\phi}\\right) \\sin\\left({\\theta}\\right) \\sinh\\left({\\rho}\\right), {\\ell} \\cos\\left({\\theta}\\right) \\sinh\\left({\\rho}\\right)\\right)\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left({\\ell} \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right) \\cosh\\left({\\rho}\\right), {\\ell} \\cosh\\left({\\rho}\\right) \\sin\\left(\\frac{{\\tau}}{{\\ell}}\\right), {\\ell} \\cos\\left({\\phi}\\right) \\sin\\left({\\theta}\\right) \\sinh\\left({\\rho}\\right), {\\ell} \\sin\\left({\\phi}\\right) \\sin\\left({\\theta}\\right) \\sinh\\left({\\rho}\\right), {\\ell} \\cos\\left({\\theta}\\right) \\sinh\\left({\\rho}\\right)\\right)$$" ], "text/plain": [ "Coordinate functions (l*cos(ta/l)*cosh(rh), l*cosh(rh)*sin(ta/l), l*cos(ph)*sin(th)*sinh(rh), l*sin(ph)*sin(th)*sinh(rh), l*cos(th)*sinh(rh)) on the Chart (M_0, (ta, rh, th, ph))" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Phi.coord_functions() # the default pair of charts (X_hyp, X23) is assumed" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left(\\cos\\left({\\tau}\\right) \\cosh\\left({\\rho}\\right), \\cosh\\left({\\rho}\\right) \\sin\\left({\\tau}\\right), \\sinh\\left({\\rho}\\right)\\right)\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left(\\cos\\left({\\tau}\\right) \\cosh\\left({\\rho}\\right), \\cosh\\left({\\rho}\\right) \\sin\\left({\\tau}\\right), \\sinh\\left({\\rho}\\right)\\right)$$" ], "text/plain": [ "(cos(ta)*cosh(rh), cosh(rh)*sin(ta), sinh(rh))" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Ug = Phi.coord_functions()[0](ta,rh,pi/2,0).subs({l:1}) # l=1 substituted to have numerical values\n", "Vg = Phi.coord_functions()[1](ta,rh,pi/2,0).subs({l:1})\n", "Xg = Phi.coord_functions()[2](ta,rh,pi/2,0).subs({l:1})\n", "Ug, Vg, Xg" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "Graphics3d Object" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hyperboloid = parametric_plot3d([Vg, Xg, Ug], (ta,0,2*pi), (rh,-2,2), color=(1.,1.,0.9))\n", "graph_hyp += hyperboloid\n", "show(graph_hyp, aspect_ratio=1, axes_labels=['V','X','U'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Spacetime metric" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As mentionned above, the AdS metric $g$ on $\\mathcal{M}$ is that induced by the flat metric $h$ on $\\mathbb{R}^{2,3}$, i.e.$g$ is the pullback of $h$ by the differentiable map $\\Phi$:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "g = M.metric()\n", "g.set( Phi.pullback(h) )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

The expression of $g$ in terms of $\\mathcal{M}$'s default frame is found to be

" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}g = -\\cosh\\left({\\rho}\\right)^{2} \\mathrm{d} {\\tau}\\otimes \\mathrm{d} {\\tau} + {\\ell}^{2} \\mathrm{d} {\\rho}\\otimes \\mathrm{d} {\\rho} + {\\ell}^{2} \\sinh\\left({\\rho}\\right)^{2} \\mathrm{d} {\\theta}\\otimes \\mathrm{d} {\\theta} + {\\ell}^{2} \\sin\\left({\\theta}\\right)^{2} \\sinh\\left({\\rho}\\right)^{2} \\mathrm{d} {\\phi}\\otimes \\mathrm{d} {\\phi}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}g = -\\cosh\\left({\\rho}\\right)^{2} \\mathrm{d} {\\tau}\\otimes \\mathrm{d} {\\tau} + {\\ell}^{2} \\mathrm{d} {\\rho}\\otimes \\mathrm{d} {\\rho} + {\\ell}^{2} \\sinh\\left({\\rho}\\right)^{2} \\mathrm{d} {\\theta}\\otimes \\mathrm{d} {\\theta} + {\\ell}^{2} \\sin\\left({\\theta}\\right)^{2} \\sinh\\left({\\rho}\\right)^{2} \\mathrm{d} {\\phi}\\otimes \\mathrm{d} {\\phi}$$" ], "text/plain": [ "g = -cosh(rh)^2 dta⊗dta + l^2 drh⊗drh + l^2*sinh(rh)^2 dth⊗dth + l^2*sin(th)^2*sinh(rh)^2 dph⊗dph" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g.display()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left(\\begin{array}{rrrr}\n", "-\\cosh\\left({\\rho}\\right)^{2} & 0 & 0 & 0 \\\\\n", "0 & {\\ell}^{2} & 0 & 0 \\\\\n", "0 & 0 & {\\ell}^{2} \\sinh\\left({\\rho}\\right)^{2} & 0 \\\\\n", "0 & 0 & 0 & {\\ell}^{2} \\sin\\left({\\theta}\\right)^{2} \\sinh\\left({\\rho}\\right)^{2}\n", "\\end{array}\\right)\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left(\\begin{array}{rrrr}\n", "-\\cosh\\left({\\rho}\\right)^{2} & 0 & 0 & 0 \\\\\n", "0 & {\\ell}^{2} & 0 & 0 \\\\\n", "0 & 0 & {\\ell}^{2} \\sinh\\left({\\rho}\\right)^{2} & 0 \\\\\n", "0 & 0 & 0 & {\\ell}^{2} \\sin\\left({\\theta}\\right)^{2} \\sinh\\left({\\rho}\\right)^{2}\n", "\\end{array}\\right)$$" ], "text/plain": [ "[ -cosh(rh)^2 0 0 0]\n", "[ 0 l^2 0 0]\n", "[ 0 0 l^2*sinh(rh)^2 0]\n", "[ 0 0 0 l^2*sin(th)^2*sinh(rh)^2]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g[:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Curvature

\n", "

The Riemann tensor of $g$ is

" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tensor field Riem(g) of type (1,3) on the 4-dimensional Lorentzian manifold M\n" ] } ], "source": [ "Riem = g.riemann()\n", "print(Riem)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{lcl} \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\tau}} \\, {\\rho} \\, {\\tau} \\, {\\rho} }^{ \\, {\\tau} \\phantom{\\, {\\rho}} \\phantom{\\, {\\tau}} \\phantom{\\, {\\rho}} } & = & -1 \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\tau}} \\, {\\theta} \\, {\\tau} \\, {\\theta} }^{ \\, {\\tau} \\phantom{\\, {\\theta}} \\phantom{\\, {\\tau}} \\phantom{\\, {\\theta}} } & = & -\\sinh\\left({\\rho}\\right)^{2} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\tau}} \\, {\\phi} \\, {\\tau} \\, {\\phi} }^{ \\, {\\tau} \\phantom{\\, {\\phi}} \\phantom{\\, {\\tau}} \\phantom{\\, {\\phi}} } & = & -\\sin\\left({\\theta}\\right)^{2} \\sinh\\left({\\rho}\\right)^{2} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\rho}} \\, {\\tau} \\, {\\tau} \\, {\\rho} }^{ \\, {\\rho} \\phantom{\\, {\\tau}} \\phantom{\\, {\\tau}} \\phantom{\\, {\\rho}} } & = & -\\frac{\\cosh\\left({\\rho}\\right)^{2}}{{\\ell}^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\rho}} \\, {\\theta} \\, {\\rho} \\, {\\theta} }^{ \\, {\\rho} \\phantom{\\, {\\theta}} \\phantom{\\, {\\rho}} \\phantom{\\, {\\theta}} } & = & -\\sinh\\left({\\rho}\\right)^{2} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\rho}} \\, {\\phi} \\, {\\rho} \\, {\\phi} }^{ \\, {\\rho} \\phantom{\\, {\\phi}} \\phantom{\\, {\\rho}} \\phantom{\\, {\\phi}} } & = & -\\sin\\left({\\theta}\\right)^{2} \\sinh\\left({\\rho}\\right)^{2} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\theta}} \\, {\\tau} \\, {\\tau} \\, {\\theta} }^{ \\, {\\theta} \\phantom{\\, {\\tau}} \\phantom{\\, {\\tau}} \\phantom{\\, {\\theta}} } & = & -\\frac{\\cosh\\left({\\rho}\\right)^{2}}{{\\ell}^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\theta}} \\, {\\rho} \\, {\\rho} \\, {\\theta} }^{ \\, {\\theta} \\phantom{\\, {\\rho}} \\phantom{\\, {\\rho}} \\phantom{\\, {\\theta}} } & = & 1 \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\theta}} \\, {\\phi} \\, {\\theta} \\, {\\phi} }^{ \\, {\\theta} \\phantom{\\, {\\phi}} \\phantom{\\, {\\theta}} \\phantom{\\, {\\phi}} } & = & -\\sin\\left({\\theta}\\right)^{2} \\sinh\\left({\\rho}\\right)^{2} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\phi}} \\, {\\tau} \\, {\\tau} \\, {\\phi} }^{ \\, {\\phi} \\phantom{\\, {\\tau}} \\phantom{\\, {\\tau}} \\phantom{\\, {\\phi}} } & = & -\\frac{\\cosh\\left({\\rho}\\right)^{2}}{{\\ell}^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\phi}} \\, {\\rho} \\, {\\rho} \\, {\\phi} }^{ \\, {\\phi} \\phantom{\\, {\\rho}} \\phantom{\\, {\\rho}} \\phantom{\\, {\\phi}} } & = & 1 \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\phi}} \\, {\\theta} \\, {\\theta} \\, {\\phi} }^{ \\, {\\phi} \\phantom{\\, {\\theta}} \\phantom{\\, {\\theta}} \\phantom{\\, {\\phi}} } & = & \\sinh\\left({\\rho}\\right)^{2} \\end{array}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{lcl} \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\tau}} \\, {\\rho} \\, {\\tau} \\, {\\rho} }^{ \\, {\\tau} \\phantom{\\, {\\rho}} \\phantom{\\, {\\tau}} \\phantom{\\, {\\rho}} } & = & -1 \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\tau}} \\, {\\theta} \\, {\\tau} \\, {\\theta} }^{ \\, {\\tau} \\phantom{\\, {\\theta}} \\phantom{\\, {\\tau}} \\phantom{\\, {\\theta}} } & = & -\\sinh\\left({\\rho}\\right)^{2} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\tau}} \\, {\\phi} \\, {\\tau} \\, {\\phi} }^{ \\, {\\tau} \\phantom{\\, {\\phi}} \\phantom{\\, {\\tau}} \\phantom{\\, {\\phi}} } & = & -\\sin\\left({\\theta}\\right)^{2} \\sinh\\left({\\rho}\\right)^{2} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\rho}} \\, {\\tau} \\, {\\tau} \\, {\\rho} }^{ \\, {\\rho} \\phantom{\\, {\\tau}} \\phantom{\\, {\\tau}} \\phantom{\\, {\\rho}} } & = & -\\frac{\\cosh\\left({\\rho}\\right)^{2}}{{\\ell}^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\rho}} \\, {\\theta} \\, {\\rho} \\, {\\theta} }^{ \\, {\\rho} \\phantom{\\, {\\theta}} \\phantom{\\, {\\rho}} \\phantom{\\, {\\theta}} } & = & -\\sinh\\left({\\rho}\\right)^{2} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\rho}} \\, {\\phi} \\, {\\rho} \\, {\\phi} }^{ \\, {\\rho} \\phantom{\\, {\\phi}} \\phantom{\\, {\\rho}} \\phantom{\\, {\\phi}} } & = & -\\sin\\left({\\theta}\\right)^{2} \\sinh\\left({\\rho}\\right)^{2} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\theta}} \\, {\\tau} \\, {\\tau} \\, {\\theta} }^{ \\, {\\theta} \\phantom{\\, {\\tau}} \\phantom{\\, {\\tau}} \\phantom{\\, {\\theta}} } & = & -\\frac{\\cosh\\left({\\rho}\\right)^{2}}{{\\ell}^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\theta}} \\, {\\rho} \\, {\\rho} \\, {\\theta} }^{ \\, {\\theta} \\phantom{\\, {\\rho}} \\phantom{\\, {\\rho}} \\phantom{\\, {\\theta}} } & = & 1 \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\theta}} \\, {\\phi} \\, {\\theta} \\, {\\phi} }^{ \\, {\\theta} \\phantom{\\, {\\phi}} \\phantom{\\, {\\theta}} \\phantom{\\, {\\phi}} } & = & -\\sin\\left({\\theta}\\right)^{2} \\sinh\\left({\\rho}\\right)^{2} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\phi}} \\, {\\tau} \\, {\\tau} \\, {\\phi} }^{ \\, {\\phi} \\phantom{\\, {\\tau}} \\phantom{\\, {\\tau}} \\phantom{\\, {\\phi}} } & = & -\\frac{\\cosh\\left({\\rho}\\right)^{2}}{{\\ell}^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\phi}} \\, {\\rho} \\, {\\rho} \\, {\\phi} }^{ \\, {\\phi} \\phantom{\\, {\\rho}} \\phantom{\\, {\\rho}} \\phantom{\\, {\\phi}} } & = & 1 \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\phi}} \\, {\\theta} \\, {\\theta} \\, {\\phi} }^{ \\, {\\phi} \\phantom{\\, {\\theta}} \\phantom{\\, {\\theta}} \\phantom{\\, {\\phi}} } & = & \\sinh\\left({\\rho}\\right)^{2} \\end{array}$$" ], "text/plain": [ "Riem(g)^ta_rh,ta,rh = -1 \n", "Riem(g)^ta_th,ta,th = -sinh(rh)^2 \n", "Riem(g)^ta_ph,ta,ph = -sin(th)^2*sinh(rh)^2 \n", "Riem(g)^rh_ta,ta,rh = -cosh(rh)^2/l^2 \n", "Riem(g)^rh_th,rh,th = -sinh(rh)^2 \n", "Riem(g)^rh_ph,rh,ph = -sin(th)^2*sinh(rh)^2 \n", "Riem(g)^th_ta,ta,th = -cosh(rh)^2/l^2 \n", "Riem(g)^th_rh,rh,th = 1 \n", "Riem(g)^th_ph,th,ph = -sin(th)^2*sinh(rh)^2 \n", "Riem(g)^ph_ta,ta,ph = -cosh(rh)^2/l^2 \n", "Riem(g)^ph_rh,rh,ph = 1 \n", "Riem(g)^ph_th,th,ph = sinh(rh)^2 " ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Riem.display_comp(only_nonredundant=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

The Ricci tensor:

" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Field of symmetric bilinear forms Ric(g) on the 4-dimensional Lorentzian manifold M\n" ] }, { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\mathrm{Ric}\\left(g\\right) = \\frac{3 \\, \\cosh\\left({\\rho}\\right)^{2}}{{\\ell}^{2}} \\mathrm{d} {\\tau}\\otimes \\mathrm{d} {\\tau} -3 \\mathrm{d} {\\rho}\\otimes \\mathrm{d} {\\rho} -3 \\, \\sinh\\left({\\rho}\\right)^{2} \\mathrm{d} {\\theta}\\otimes \\mathrm{d} {\\theta} -3 \\, \\sin\\left({\\theta}\\right)^{2} \\sinh\\left({\\rho}\\right)^{2} \\mathrm{d} {\\phi}\\otimes \\mathrm{d} {\\phi}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\mathrm{Ric}\\left(g\\right) = \\frac{3 \\, \\cosh\\left({\\rho}\\right)^{2}}{{\\ell}^{2}} \\mathrm{d} {\\tau}\\otimes \\mathrm{d} {\\tau} -3 \\mathrm{d} {\\rho}\\otimes \\mathrm{d} {\\rho} -3 \\, \\sinh\\left({\\rho}\\right)^{2} \\mathrm{d} {\\theta}\\otimes \\mathrm{d} {\\theta} -3 \\, \\sin\\left({\\theta}\\right)^{2} \\sinh\\left({\\rho}\\right)^{2} \\mathrm{d} {\\phi}\\otimes \\mathrm{d} {\\phi}$$" ], "text/plain": [ "Ric(g) = 3*cosh(rh)^2/l^2 dta⊗dta - 3 drh⊗drh - 3*sinh(rh)^2 dth⊗dth - 3*sin(th)^2*sinh(rh)^2 dph⊗dph" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Ric = g.ricci()\n", "print(Ric)\n", "Ric.display()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left(\\begin{array}{rrrr}\n", "\\frac{3 \\, \\cosh\\left({\\rho}\\right)^{2}}{{\\ell}^{2}} & 0 & 0 & 0 \\\\\n", "0 & -3 & 0 & 0 \\\\\n", "0 & 0 & -3 \\, \\sinh\\left({\\rho}\\right)^{2} & 0 \\\\\n", "0 & 0 & 0 & -3 \\, \\sin\\left({\\theta}\\right)^{2} \\sinh\\left({\\rho}\\right)^{2}\n", "\\end{array}\\right)\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left(\\begin{array}{rrrr}\n", "\\frac{3 \\, \\cosh\\left({\\rho}\\right)^{2}}{{\\ell}^{2}} & 0 & 0 & 0 \\\\\n", "0 & -3 & 0 & 0 \\\\\n", "0 & 0 & -3 \\, \\sinh\\left({\\rho}\\right)^{2} & 0 \\\\\n", "0 & 0 & 0 & -3 \\, \\sin\\left({\\theta}\\right)^{2} \\sinh\\left({\\rho}\\right)^{2}\n", "\\end{array}\\right)$$" ], "text/plain": [ "[ 3*cosh(rh)^2/l^2 0 0 0]\n", "[ 0 -3 0 0]\n", "[ 0 0 -3*sinh(rh)^2 0]\n", "[ 0 0 0 -3*sin(th)^2*sinh(rh)^2]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Ric[:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

The Ricci scalar:

" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Scalar field r(g) on the 4-dimensional Lorentzian manifold M\n" ] }, { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} \\mathrm{r}\\left(g\\right):& \\mathcal{M} & \\longrightarrow & \\mathbb{R} \\\\ \\mbox{on}\\ \\mathcal{M}_0 : & \\left({\\tau}, {\\rho}, {\\theta}, {\\phi}\\right) & \\longmapsto & -\\frac{12}{{\\ell}^{2}} \\end{array}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} \\mathrm{r}\\left(g\\right):& \\mathcal{M} & \\longrightarrow & \\mathbb{R} \\\\ \\mbox{on}\\ \\mathcal{M}_0 : & \\left({\\tau}, {\\rho}, {\\theta}, {\\phi}\\right) & \\longmapsto & -\\frac{12}{{\\ell}^{2}} \\end{array}$$" ], "text/plain": [ "r(g): M → ℝ\n", "on M_0: (ta, rh, th, ph) ↦ -12/l^2" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "R = g.ricci_scalar()\n", "print(R)\n", "R.display()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We recover the fact that AdS spacetime has a constant curvature. It is indeed a **maximally symmetric space**. In particular, the Riemann tensor is expressible as\n", "$$ R^i_{\\ \\, jlk} = \\frac{R}{n(n-1)} \\left( \\delta^i_{\\ \\, k} g_{jl} - \\delta^i_{\\ \\, l} g_{jk} \\right), $$\n", "where $n$ is the dimension of $\\mathcal{M}$: $n=4$ in the present case. Let us check this formula here, under the form $R^i_{\\ \\, jlk} = -\\frac{R}{6} g_{j[k} \\delta^i_{\\ \\, l]}$:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\mathrm{True}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\mathrm{True}$$" ], "text/plain": [ "True" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "delta = M.tangent_identity_field() \n", "Riem == - (R/6)*(g*delta).antisymmetrize(2,3) # 2,3 = last positions of the \n", " # type-(1,3) tensor g*delta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We may also check that AdS metric is a solution of the vacuum **Einstein equation** with (negative) cosmological constant $\\Lambda = - 3/\\ell^2$:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\mathrm{True}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\mathrm{True}$$" ], "text/plain": [ "True" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Lambda = -3/l^2\n", "Ric - 1/2*R*g + Lambda*g == 0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Radial null geodesics\n", "\n", "Null geodesics that are radial with respect to coordinates $(\\tau,\\rho,\\theta,\\phi)$ obey\n", "$$ \\tau = \\pm 2 \\ell \\left( \\mathrm{atan} \\left(\\mathrm{e}^\\rho\\right) - \\frac{\\pi}{4} \\right) + \\tau_0,$$\n", "where $\\tau_0$ is a constant (the value of $\\tau$ at $\\rho=0$). Note that, due to the homogeneity of AdS spacetime, any null geodesic is a \"radial\" geodesic with respect to some coordinate system $(\\tau',\\rho',\\theta',\\phi')$, as in Minkowski spacetime, any null geodesic is a straight line and one can always find a Minkowskian coordinate system $(t',x',y',z')$ with respect to which the null geodesic is radial.\n", "\n", "Let us consider two finite families of radial null geodesics having $\\theta=\\pi/2$ and $\\phi=0$ or $\\pi$: \n", "- `null_geod1` has $\\phi=\\pi$ when $\\tau< 0$ and $\\phi=0$ when $\\tau>0$\n", "- `null_geod2` has $\\phi=0$ when $\\tau<0$ and $\\phi=\\pi$ when $\\tau>0$" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "lamb = var('lamb', latex_name=r'\\lambda')\n", "null_geod1 = [M.curve({X_hyp: [2*sgn(lamb)*l*(atan(exp(abs(lamb))) - pi/4) + 2*pi*(i-4)/8, \n", " abs(lamb), pi/2, pi*unit_step(-lamb)]}, \n", " (lamb, -oo, +oo)) for i in range(9)]\n", "null_geod2 = [M.curve({X_hyp: [2*sgn(lamb)*l*(atan(exp(abs(lamb))) - pi/4) + 2*pi*(i-4)/8, \n", " abs(lamb), pi/2, pi*unit_step(lamb)]}, \n", " (lamb, -oo, +oo)) for i in range(9)]\n", "null_geods = null_geod1 + null_geod2" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Curve in the 4-dimensional Lorentzian manifold M\n" ] } ], "source": [ "print(null_geods[0])" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} & \\Bold{R} & \\longrightarrow & \\mathcal{M} \\\\ & {\\lambda} & \\longmapsto & \\left({\\tau}, {\\rho}, {\\theta}, {\\phi}\\right) = \\left(-\\pi - \\frac{1}{2} \\, {\\left(\\pi - 4 \\, \\arctan\\left(e^{\\left({\\left| {\\lambda} \\right|}\\right)}\\right)\\right)} {\\ell} \\mathrm{sgn}\\left({\\lambda}\\right), {\\left| {\\lambda} \\right|}, \\frac{1}{2} \\, \\pi, \\pi \\mathrm{u}\\left(-{\\lambda}\\right)\\right) \\end{array}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} & \\Bold{R} & \\longrightarrow & \\mathcal{M} \\\\ & {\\lambda} & \\longmapsto & \\left({\\tau}, {\\rho}, {\\theta}, {\\phi}\\right) = \\left(-\\pi - \\frac{1}{2} \\, {\\left(\\pi - 4 \\, \\arctan\\left(e^{\\left({\\left| {\\lambda} \\right|}\\right)}\\right)\\right)} {\\ell} \\mathrm{sgn}\\left({\\lambda}\\right), {\\left| {\\lambda} \\right|}, \\frac{1}{2} \\, \\pi, \\pi \\mathrm{u}\\left(-{\\lambda}\\right)\\right) \\end{array}$$" ], "text/plain": [ "ℝ → M\n", " lamb ↦ (ta, rh, th, ph) = (-pi - 1/2*(pi - 4*arctan(e^abs(lamb)))*l*sgn(lamb), abs(lamb), 1/2*pi, pi*unit_step(-lamb))" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "null_geods[0].display()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} & \\Bold{R} & \\longrightarrow & \\mathcal{M} \\\\ & {\\lambda} & \\longmapsto & \\left({\\tau}, {\\rho}, {\\theta}, {\\phi}\\right) = \\left(-\\pi - \\frac{1}{2} \\, {\\left(\\pi - 4 \\, \\arctan\\left(e^{\\left({\\left| {\\lambda} \\right|}\\right)}\\right)\\right)} {\\ell} \\mathrm{sgn}\\left({\\lambda}\\right), {\\left| {\\lambda} \\right|}, \\frac{1}{2} \\, \\pi, \\pi \\mathrm{u}\\left({\\lambda}\\right)\\right) \\end{array}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} & \\Bold{R} & \\longrightarrow & \\mathcal{M} \\\\ & {\\lambda} & \\longmapsto & \\left({\\tau}, {\\rho}, {\\theta}, {\\phi}\\right) = \\left(-\\pi - \\frac{1}{2} \\, {\\left(\\pi - 4 \\, \\arctan\\left(e^{\\left({\\left| {\\lambda} \\right|}\\right)}\\right)\\right)} {\\ell} \\mathrm{sgn}\\left({\\lambda}\\right), {\\left| {\\lambda} \\right|}, \\frac{1}{2} \\, \\pi, \\pi \\mathrm{u}\\left({\\lambda}\\right)\\right) \\end{array}$$" ], "text/plain": [ "ℝ → M\n", " lamb ↦ (ta, rh, th, ph) = (-pi - 1/2*(pi - 4*arctan(e^abs(lamb)))*l*sgn(lamb), abs(lamb), 1/2*pi, pi*unit_step(lamb))" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "null_geods[9].display()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To graphically display these geodesics, we introduce a Cartesian-like coordinate system\n", "$(\\tau,x_\\rho,y_\\rho,z_\\rho)$ linked to $(\\tau,\\rho,\\theta,\\phi)$ by the standard formulas:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left\\{\\begin{array}{lcl} {\\tau} & = & {\\tau} \\\\ {x_\\rho} & = & {\\rho} \\cos\\left({\\phi}\\right) \\sin\\left({\\theta}\\right) \\\\ {y_\\rho} & = & {\\rho} \\sin\\left({\\phi}\\right) \\sin\\left({\\theta}\\right) \\\\ {z_\\rho} & = & {\\rho} \\cos\\left({\\theta}\\right) \\end{array}\\right.\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left\\{\\begin{array}{lcl} {\\tau} & = & {\\tau} \\\\ {x_\\rho} & = & {\\rho} \\cos\\left({\\phi}\\right) \\sin\\left({\\theta}\\right) \\\\ {y_\\rho} & = & {\\rho} \\sin\\left({\\phi}\\right) \\sin\\left({\\theta}\\right) \\\\ {z_\\rho} & = & {\\rho} \\cos\\left({\\theta}\\right) \\end{array}\\right.$$" ], "text/plain": [ "ta = ta\n", "x_rho = rh*cos(ph)*sin(th)\n", "y_rho = rh*sin(ph)*sin(th)\n", "z_rho = rh*cos(th)" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_hyp_graph. = M0.chart(r'ta:\\tau x_rho:x_\\rho y_rho:y_\\rho z_rho:z_\\rho')\n", "hyp_to_hyp_graph = X_hyp.transition_map(X_hyp_graph, [ta, rh*sin(th)*cos(ph), \n", " rh*sin(th)*sin(ph), rh*cos(th)])\n", "hyp_to_hyp_graph.display()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us plot the null geodesics in terms of the coordinates $(\\tau,x_\\rho)$:" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "Graphics object consisting of 36 graphics primitives" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "graph_2d = Graphics()\n", "for geod in null_geods:\n", " geod.expr(geod.domain().canonical_chart(), X_hyp_graph)\n", " graph_2d += geod.plot(X_hyp_graph, ambient_coords=(x_rho,ta), prange=(-4,4),\n", " parameters={l:1}, color='green', thickness=1.5)\n", "graph_2d += X_hyp_graph.plot(X_hyp_graph, ambient_coords=(x_rho,ta), \n", " fixed_coords={th:0, ph:pi}, \n", " ranges={ta:(-pi,pi), x_rho:(-4,4)}, \n", " number_values={ta: 9, x_rho: 9},\n", " color={ta:'red', x_rho:'grey'}, parameters={l:1})\n", "show(graph_2d, aspect_ratio=1, ymin=-pi, ymax=pi)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also get a 3D view of the radial null geodesics via the isometric immersion $\\Phi$:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "Graphics3d Object" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "graph_3d = Graphics()\n", "for geod in null_geods:\n", " graph_3d += geod.plot(X23, mapping=Phi, ambient_coords=(V,X,U), prange=(-2,2),\n", " parameters={l:1}, color='green', thickness=2, \n", " plot_points=20, label_axes=False)\n", "show(graph_3d+graph_hyp, aspect_ratio=1, axes_labels=['V','X','U'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We notice that the image by $\\Phi$ of the null geodesics are straight lines of $\\mathbb{R}^{2,3}$.\n", "This is not surprising since $\\Phi$ is an isometric immersion and the null geodesics of\n", "$\\mathbb{R}^{2,3}$ are straight lines. Note that the two considered families of null geodesics of $\\mathrm{AdS}_4$ \n", "rule the hyperboloid (remember that a hyperboloid of one sheet is a ruled surface). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A timelike geodesic\n", "\n", "Let us consider a timelike geodesic:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} & \\Bold{R} & \\longrightarrow & \\mathcal{M} \\\\ & {\\tau} & \\longmapsto & \\left({\\tau}, {\\rho}, {\\theta}, {\\phi}\\right) = \\left({\\tau}, {\\left| \\operatorname{artanh}\\left(\\frac{4}{5} \\, \\sin\\left(\\frac{{\\tau}}{{\\ell}}\\right)\\right) \\right|}, \\frac{1}{2} \\, \\pi, \\pi \\mathrm{u}\\left(\\operatorname{frac}\\left(\\frac{{\\tau}}{2 \\, \\pi {\\ell}}\\right) - \\frac{1}{2}\\right)\\right) \\\\ & {\\tau} & \\longmapsto & \\left({\\tau}, {x_\\rho}, {y_\\rho}, {z_\\rho}\\right) = \\left({\\tau}, {\\left| \\operatorname{artanh}\\left(\\frac{4}{5} \\, \\sin\\left(\\frac{{\\tau}}{{\\ell}}\\right)\\right) \\right|} \\cos\\left(\\pi \\mathrm{u}\\left(\\operatorname{frac}\\left(\\frac{{\\tau}}{2 \\, \\pi {\\ell}}\\right) - \\frac{1}{2}\\right)\\right), {\\left| \\operatorname{artanh}\\left(\\frac{4}{5} \\, \\sin\\left(\\frac{{\\tau}}{{\\ell}}\\right)\\right) \\right|} \\sin\\left(\\pi \\mathrm{u}\\left(\\operatorname{frac}\\left(\\frac{{\\tau}}{2 \\, \\pi {\\ell}}\\right) - \\frac{1}{2}\\right)\\right), 0\\right) \\end{array}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} & \\Bold{R} & \\longrightarrow & \\mathcal{M} \\\\ & {\\tau} & \\longmapsto & \\left({\\tau}, {\\rho}, {\\theta}, {\\phi}\\right) = \\left({\\tau}, {\\left| \\operatorname{artanh}\\left(\\frac{4}{5} \\, \\sin\\left(\\frac{{\\tau}}{{\\ell}}\\right)\\right) \\right|}, \\frac{1}{2} \\, \\pi, \\pi \\mathrm{u}\\left(\\operatorname{frac}\\left(\\frac{{\\tau}}{2 \\, \\pi {\\ell}}\\right) - \\frac{1}{2}\\right)\\right) \\\\ & {\\tau} & \\longmapsto & \\left({\\tau}, {x_\\rho}, {y_\\rho}, {z_\\rho}\\right) = \\left({\\tau}, {\\left| \\operatorname{artanh}\\left(\\frac{4}{5} \\, \\sin\\left(\\frac{{\\tau}}{{\\ell}}\\right)\\right) \\right|} \\cos\\left(\\pi \\mathrm{u}\\left(\\operatorname{frac}\\left(\\frac{{\\tau}}{2 \\, \\pi {\\ell}}\\right) - \\frac{1}{2}\\right)\\right), {\\left| \\operatorname{artanh}\\left(\\frac{4}{5} \\, \\sin\\left(\\frac{{\\tau}}{{\\ell}}\\right)\\right) \\right|} \\sin\\left(\\pi \\mathrm{u}\\left(\\operatorname{frac}\\left(\\frac{{\\tau}}{2 \\, \\pi {\\ell}}\\right) - \\frac{1}{2}\\right)\\right), 0\\right) \\end{array}$$" ], "text/plain": [ "ℝ → M\n", " ta ↦ (ta, rh, th, ph) = (ta, abs(arctanh(4/5*sin(ta/l))), 1/2*pi, pi*unit_step(frac(1/2*ta/(pi*l)) - 1/2))\n", " ta ↦ (ta, x_rho, y_rho, z_rho) = (ta, abs(arctanh(4/5*sin(ta/l)))*cos(pi*unit_step(frac(1/2*ta/(pi*l)) - 1/2)), abs(arctanh(4/5*sin(ta/l)))*sin(pi*unit_step(frac(1/2*ta/(pi*l)) - 1/2)), 0)" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "time_geod = M.curve({X_hyp: [ta, abs(atanh(4*sin(ta/l)/5)), pi/2, \n", " pi*unit_step(frac(ta/(2*pi*l))-1/2)]}, \n", " (ta, -oo, +oo))\n", "time_geod.display()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and draw it in terms of the $(\\tau,x_\\rho)$ coordinates:" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "Graphics object consisting of 37 graphics primitives" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "graph = time_geod.plot(X_hyp_graph, ambient_coords=(x_rho,ta), plot_points=800,\n", " parameters={l:1}, color='purple', thickness=2)\n", "show(graph+graph_2d, aspect_ratio=1, ymin=-pi, ymax=pi)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us superpose the timelike geodesic to the 3D plot obtained via the immersion $\\Phi$ of AdS$_4$ in $\\mathbb{R}^{2,3}$:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "Graphics3d Object" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "graph_3d += time_geod.plot(X23, mapping=Phi, ambient_coords=(V,X,U), prange=(0,2*pi),\n", " parameters={l:1}, color='purple', thickness=2, \n", " label_axes=False)\n", "show(graph_3d+graph_hyp, aspect_ratio=1, axes_labels=['V','X','U'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We notice that the immersed timelike geodesic looks like an ellipse. It is actually a *circle* of $\\mathbb{R}^{2,3}$, as we can see by considering its restriction to $\\tau\\in(0,\\pi)$ (this avoids absolute values and step functions and therefore ease the simplifications):" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} & \\left(0, \\pi\\right) & \\longrightarrow & \\mathcal{M} \\\\ & {\\tau} & \\longmapsto & \\left({\\tau}, {\\rho}, {\\theta}, {\\phi}\\right) = \\left({\\tau}, \\operatorname{artanh}\\left(\\frac{4}{5} \\, \\sin\\left(\\frac{{\\tau}}{{\\ell}}\\right)\\right), \\frac{1}{2} \\, \\pi, 0\\right) \\\\ & {\\tau} & \\longmapsto & \\left({\\tau}, {x_\\rho}, {y_\\rho}, {z_\\rho}\\right) = \\left({\\tau}, \\operatorname{artanh}\\left(\\frac{4}{5} \\, \\sin\\left(\\frac{{\\tau}}{{\\ell}}\\right)\\right), 0, 0\\right) \\end{array}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} & \\left(0, \\pi\\right) & \\longrightarrow & \\mathcal{M} \\\\ & {\\tau} & \\longmapsto & \\left({\\tau}, {\\rho}, {\\theta}, {\\phi}\\right) = \\left({\\tau}, \\operatorname{artanh}\\left(\\frac{4}{5} \\, \\sin\\left(\\frac{{\\tau}}{{\\ell}}\\right)\\right), \\frac{1}{2} \\, \\pi, 0\\right) \\\\ & {\\tau} & \\longmapsto & \\left({\\tau}, {x_\\rho}, {y_\\rho}, {z_\\rho}\\right) = \\left({\\tau}, \\operatorname{artanh}\\left(\\frac{4}{5} \\, \\sin\\left(\\frac{{\\tau}}{{\\ell}}\\right)\\right), 0, 0\\right) \\end{array}$$" ], "text/plain": [ "(0, pi) → M\n", " ta ↦ (ta, rh, th, ph) = (ta, arctanh(4/5*sin(ta/l)), 1/2*pi, 0)\n", " ta ↦ (ta, x_rho, y_rho, z_rho) = (ta, arctanh(4/5*sin(ta/l)), 0, 0)" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "time_geod_partial = M.curve({X_hyp: [ta, atanh(4*sin(ta/l)/5), pi/2, 0]}, \n", " (ta, 0, pi))\n", "time_geod_partial.display()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The immersed curve:" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} & \\left(0, \\pi\\right) & \\longrightarrow & \\mathbb{R}^{2,3} \\\\ & {\\tau} & \\longmapsto & \\left(U, V, X, Y, Z\\right) = \\left(\\frac{5 \\, {\\ell} \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right)}{\\sqrt{16 \\, \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right)^{2} + 9}}, \\frac{5 \\, {\\ell} \\sin\\left(\\frac{{\\tau}}{{\\ell}}\\right)}{\\sqrt{16 \\, \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right)^{2} + 9}}, \\frac{4 \\, {\\ell} \\sin\\left(\\frac{{\\tau}}{{\\ell}}\\right)}{\\sqrt{16 \\, \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right)^{2} + 9}}, 0, 0\\right) \\end{array}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} & \\left(0, \\pi\\right) & \\longrightarrow & \\mathbb{R}^{2,3} \\\\ & {\\tau} & \\longmapsto & \\left(U, V, X, Y, Z\\right) = \\left(\\frac{5 \\, {\\ell} \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right)}{\\sqrt{16 \\, \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right)^{2} + 9}}, \\frac{5 \\, {\\ell} \\sin\\left(\\frac{{\\tau}}{{\\ell}}\\right)}{\\sqrt{16 \\, \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right)^{2} + 9}}, \\frac{4 \\, {\\ell} \\sin\\left(\\frac{{\\tau}}{{\\ell}}\\right)}{\\sqrt{16 \\, \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right)^{2} + 9}}, 0, 0\\right) \\end{array}$$" ], "text/plain": [ "(0, pi) → R23\n", " ta ↦ (U, V, X, Y, Z) = (5*l*cos(ta/l)/sqrt(16*cos(ta/l)^2 + 9), 5*l*sin(ta/l)/sqrt(16*cos(ta/l)^2 + 9), 4*l*sin(ta/l)/sqrt(16*cos(ta/l)^2 + 9), 0, 0)" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(Phi*time_geod_partial).display()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The position vector with respect to the origin of $\\mathbb{R}^{2,3}$:" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}v = \\left( \\frac{5 \\, {\\ell} \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right)}{\\sqrt{16 \\, \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right)^{2} + 9}} \\right) \\frac{\\partial}{\\partial U } + \\left( \\frac{5 \\, {\\ell} \\sin\\left(\\frac{{\\tau}}{{\\ell}}\\right)}{\\sqrt{16 \\, \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right)^{2} + 9}} \\right) \\frac{\\partial}{\\partial V } + \\left( \\frac{4 \\, {\\ell} \\sin\\left(\\frac{{\\tau}}{{\\ell}}\\right)}{\\sqrt{16 \\, \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right)^{2} + 9}} \\right) \\frac{\\partial}{\\partial X }\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}v = \\left( \\frac{5 \\, {\\ell} \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right)}{\\sqrt{16 \\, \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right)^{2} + 9}} \\right) \\frac{\\partial}{\\partial U } + \\left( \\frac{5 \\, {\\ell} \\sin\\left(\\frac{{\\tau}}{{\\ell}}\\right)}{\\sqrt{16 \\, \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right)^{2} + 9}} \\right) \\frac{\\partial}{\\partial V } + \\left( \\frac{4 \\, {\\ell} \\sin\\left(\\frac{{\\tau}}{{\\ell}}\\right)}{\\sqrt{16 \\, \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right)^{2} + 9}} \\right) \\frac{\\partial}{\\partial X }$$" ], "text/plain": [ "v = 5*l*cos(ta/l)/sqrt(16*cos(ta/l)^2 + 9) ∂/∂U + 5*l*sin(ta/l)/sqrt(16*cos(ta/l)^2 + 9) ∂/∂V + 4*l*sin(ta/l)/sqrt(16*cos(ta/l)^2 + 9) ∂/∂X" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v = R23.vector_field(name='v')\n", "v[:] = (Phi*time_geod_partial).expr()\n", "v.display()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} h\\left(v,v\\right):& \\mathbb{R}^{2,3} & \\longrightarrow & \\mathbb{R} \\\\ & \\left(U, V, X, Y, Z\\right) & \\longmapsto & -{\\ell}^{2} \\end{array}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} h\\left(v,v\\right):& \\mathbb{R}^{2,3} & \\longrightarrow & \\mathbb{R} \\\\ & \\left(U, V, X, Y, Z\\right) & \\longmapsto & -{\\ell}^{2} \\end{array}$$" ], "text/plain": [ "h(v,v): R23 → ℝ\n", " (U, V, X, Y, Z) ↦ -l^2" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "h(v,v).display()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since $v$ has a constant (negative) squared norm, we conclude that the immersed timelike geodesic is a \"circle\" of $\\mathbb{R}^{2,3}$. This circle is nothing but the intersection of the hyperboloid with a 2-plane through the origin. Note also that the straight line representing the immersed null geodesics are also intersections of the hyperboloid with some 2-planes through the origin, but with a different inclination. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## \"Static\" coordinates\n", "Let us introduce coordinates $(\\tau,R,\\theta,\\phi)$ on the AdS spacetime via the simple coordinate change\n", "$$R = \\ell \\sinh(\\rho) $$\n", "Despite the $(\\tau,\\rho,\\theta,\\phi)$ coordinates are as adapted to the spacetime staticity as the $(\\tau,R,\\theta,\\phi)$ coordinates, the latter ones are usually called \"static\" coordinates." ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Chart (M_0, (ta, R, th, ph))\n" ] }, { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left(\\mathcal{M}_0,({\\tau}, R, {\\theta}, {\\phi})\\right)\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left(\\mathcal{M}_0,({\\tau}, R, {\\theta}, {\\phi})\\right)$$" ], "text/plain": [ "Chart (M_0, (ta, R, th, ph))" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_stat. = M0.chart(r'ta:\\tau R:(0,+oo) th:(0,pi):\\theta ph:(0,2*pi):\\phi')\n", "print(X_stat); X_stat" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}{\\tau} :\\ \\left( -\\infty, +\\infty \\right) ;\\quad R :\\ \\left( 0 , +\\infty \\right) ;\\quad {\\theta} :\\ \\left( 0 , \\pi \\right) ;\\quad {\\phi} :\\ \\left( 0 , 2 \\, \\pi \\right)\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}{\\tau} :\\ \\left( -\\infty, +\\infty \\right) ;\\quad R :\\ \\left( 0 , +\\infty \\right) ;\\quad {\\theta} :\\ \\left( 0 , \\pi \\right) ;\\quad {\\phi} :\\ \\left( 0 , 2 \\, \\pi \\right)$$" ], "text/plain": [ "ta: (-oo, +oo); R: (0, +oo); th: (0, pi); ph: (0, 2*pi)" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_stat.coord_range()" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left\\{\\begin{array}{lcl} {\\tau} & = & {\\tau} \\\\ R & = & {\\ell} \\sinh\\left({\\rho}\\right) \\\\ {\\theta} & = & {\\theta} \\\\ {\\phi} & = & {\\phi} \\end{array}\\right.\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left\\{\\begin{array}{lcl} {\\tau} & = & {\\tau} \\\\ R & = & {\\ell} \\sinh\\left({\\rho}\\right) \\\\ {\\theta} & = & {\\theta} \\\\ {\\phi} & = & {\\phi} \\end{array}\\right.$$" ], "text/plain": [ "ta = ta\n", "R = l*sinh(rh)\n", "th = th\n", "ph = ph" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hyp_to_stat = X_hyp.transition_map(X_stat, [ta, l*sinh(rh), th, ph])\n", "hyp_to_stat.display()" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Check of the inverse coordinate transformation:\n", " ta == ta *passed*\n", " rh == arcsinh(sinh(rh)) **failed**\n", " th == th *passed*\n", " ph == ph *passed*\n", " ta == ta *passed*\n", " R == R *passed*\n", " th == th *passed*\n", " ph == ph *passed*\n", "NB: a failed report can reflect a mere lack of simplification.\n" ] }, { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left\\{\\begin{array}{lcl} {\\tau} & = & {\\tau} \\\\ {\\rho} & = & \\operatorname{arsinh}\\left(\\frac{R}{{\\ell}}\\right) \\\\ {\\theta} & = & {\\theta} \\\\ {\\phi} & = & {\\phi} \\end{array}\\right.\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left\\{\\begin{array}{lcl} {\\tau} & = & {\\tau} \\\\ {\\rho} & = & \\operatorname{arsinh}\\left(\\frac{R}{{\\ell}}\\right) \\\\ {\\theta} & = & {\\theta} \\\\ {\\phi} & = & {\\phi} \\end{array}\\right.$$" ], "text/plain": [ "ta = ta\n", "rh = arcsinh(R/l)\n", "th = th\n", "ph = ph" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hyp_to_stat.set_inverse(ta, asinh(R/l), th, ph)\n", "stat_to_hyp = hyp_to_stat.inverse()\n", "stat_to_hyp.display()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The expression of the metric tensor in the new coordinates is" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}g = \\left( -\\frac{R^{2} + {\\ell}^{2}}{{\\ell}^{2}} \\right) \\mathrm{d} {\\tau}\\otimes \\mathrm{d} {\\tau} + \\left( \\frac{{\\ell}^{2}}{R^{2} + {\\ell}^{2}} \\right) \\mathrm{d} R\\otimes \\mathrm{d} R + R^{2} \\mathrm{d} {\\theta}\\otimes \\mathrm{d} {\\theta} + R^{2} \\sin\\left({\\theta}\\right)^{2} \\mathrm{d} {\\phi}\\otimes \\mathrm{d} {\\phi}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}g = \\left( -\\frac{R^{2} + {\\ell}^{2}}{{\\ell}^{2}} \\right) \\mathrm{d} {\\tau}\\otimes \\mathrm{d} {\\tau} + \\left( \\frac{{\\ell}^{2}}{R^{2} + {\\ell}^{2}} \\right) \\mathrm{d} R\\otimes \\mathrm{d} R + R^{2} \\mathrm{d} {\\theta}\\otimes \\mathrm{d} {\\theta} + R^{2} \\sin\\left({\\theta}\\right)^{2} \\mathrm{d} {\\phi}\\otimes \\mathrm{d} {\\phi}$$" ], "text/plain": [ "g = -(R^2 + l^2)/l^2 dta⊗dta + l^2/(R^2 + l^2) dR⊗dR + R^2 dth⊗dth + R^2*sin(th)^2 dph⊗dph" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g.display(X_stat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similarly, the expression of the Riemann tensor is" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{lcl} \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\tau}} \\, R \\, {\\tau} \\, R }^{ \\, {\\tau} \\phantom{\\, R} \\phantom{\\, {\\tau}} \\phantom{\\, R} } & = & -\\frac{1}{R^{2} + {\\ell}^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\tau}} \\, {\\theta} \\, {\\tau} \\, {\\theta} }^{ \\, {\\tau} \\phantom{\\, {\\theta}} \\phantom{\\, {\\tau}} \\phantom{\\, {\\theta}} } & = & -\\frac{R^{2}}{{\\ell}^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\tau}} \\, {\\phi} \\, {\\tau} \\, {\\phi} }^{ \\, {\\tau} \\phantom{\\, {\\phi}} \\phantom{\\, {\\tau}} \\phantom{\\, {\\phi}} } & = & -\\frac{R^{2} \\sin\\left({\\theta}\\right)^{2}}{{\\ell}^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, R} \\, {\\tau} \\, {\\tau} \\, R }^{ \\, R \\phantom{\\, {\\tau}} \\phantom{\\, {\\tau}} \\phantom{\\, R} } & = & -\\frac{R^{2} + {\\ell}^{2}}{{\\ell}^{4}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, R} \\, {\\theta} \\, R \\, {\\theta} }^{ \\, R \\phantom{\\, {\\theta}} \\phantom{\\, R} \\phantom{\\, {\\theta}} } & = & -\\frac{R^{2}}{{\\ell}^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, R} \\, {\\phi} \\, R \\, {\\phi} }^{ \\, R \\phantom{\\, {\\phi}} \\phantom{\\, R} \\phantom{\\, {\\phi}} } & = & -\\frac{R^{2} \\sin\\left({\\theta}\\right)^{2}}{{\\ell}^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\theta}} \\, {\\tau} \\, {\\tau} \\, {\\theta} }^{ \\, {\\theta} \\phantom{\\, {\\tau}} \\phantom{\\, {\\tau}} \\phantom{\\, {\\theta}} } & = & -\\frac{R^{2} + {\\ell}^{2}}{{\\ell}^{4}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\theta}} \\, R \\, R \\, {\\theta} }^{ \\, {\\theta} \\phantom{\\, R} \\phantom{\\, R} \\phantom{\\, {\\theta}} } & = & \\frac{1}{R^{2} + {\\ell}^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\theta}} \\, {\\phi} \\, {\\theta} \\, {\\phi} }^{ \\, {\\theta} \\phantom{\\, {\\phi}} \\phantom{\\, {\\theta}} \\phantom{\\, {\\phi}} } & = & -\\frac{R^{2} \\sin\\left({\\theta}\\right)^{2}}{{\\ell}^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\phi}} \\, {\\tau} \\, {\\tau} \\, {\\phi} }^{ \\, {\\phi} \\phantom{\\, {\\tau}} \\phantom{\\, {\\tau}} \\phantom{\\, {\\phi}} } & = & -\\frac{R^{2} + {\\ell}^{2}}{{\\ell}^{4}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\phi}} \\, R \\, R \\, {\\phi} }^{ \\, {\\phi} \\phantom{\\, R} \\phantom{\\, R} \\phantom{\\, {\\phi}} } & = & \\frac{1}{R^{2} + {\\ell}^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\phi}} \\, {\\theta} \\, {\\theta} \\, {\\phi} }^{ \\, {\\phi} \\phantom{\\, {\\theta}} \\phantom{\\, {\\theta}} \\phantom{\\, {\\phi}} } & = & \\frac{R^{2}}{{\\ell}^{2}} \\end{array}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{lcl} \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\tau}} \\, R \\, {\\tau} \\, R }^{ \\, {\\tau} \\phantom{\\, R} \\phantom{\\, {\\tau}} \\phantom{\\, R} } & = & -\\frac{1}{R^{2} + {\\ell}^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\tau}} \\, {\\theta} \\, {\\tau} \\, {\\theta} }^{ \\, {\\tau} \\phantom{\\, {\\theta}} \\phantom{\\, {\\tau}} \\phantom{\\, {\\theta}} } & = & -\\frac{R^{2}}{{\\ell}^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\tau}} \\, {\\phi} \\, {\\tau} \\, {\\phi} }^{ \\, {\\tau} \\phantom{\\, {\\phi}} \\phantom{\\, {\\tau}} \\phantom{\\, {\\phi}} } & = & -\\frac{R^{2} \\sin\\left({\\theta}\\right)^{2}}{{\\ell}^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, R} \\, {\\tau} \\, {\\tau} \\, R }^{ \\, R \\phantom{\\, {\\tau}} \\phantom{\\, {\\tau}} \\phantom{\\, R} } & = & -\\frac{R^{2} + {\\ell}^{2}}{{\\ell}^{4}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, R} \\, {\\theta} \\, R \\, {\\theta} }^{ \\, R \\phantom{\\, {\\theta}} \\phantom{\\, R} \\phantom{\\, {\\theta}} } & = & -\\frac{R^{2}}{{\\ell}^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, R} \\, {\\phi} \\, R \\, {\\phi} }^{ \\, R \\phantom{\\, {\\phi}} \\phantom{\\, R} \\phantom{\\, {\\phi}} } & = & -\\frac{R^{2} \\sin\\left({\\theta}\\right)^{2}}{{\\ell}^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\theta}} \\, {\\tau} \\, {\\tau} \\, {\\theta} }^{ \\, {\\theta} \\phantom{\\, {\\tau}} \\phantom{\\, {\\tau}} \\phantom{\\, {\\theta}} } & = & -\\frac{R^{2} + {\\ell}^{2}}{{\\ell}^{4}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\theta}} \\, R \\, R \\, {\\theta} }^{ \\, {\\theta} \\phantom{\\, R} \\phantom{\\, R} \\phantom{\\, {\\theta}} } & = & \\frac{1}{R^{2} + {\\ell}^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\theta}} \\, {\\phi} \\, {\\theta} \\, {\\phi} }^{ \\, {\\theta} \\phantom{\\, {\\phi}} \\phantom{\\, {\\theta}} \\phantom{\\, {\\phi}} } & = & -\\frac{R^{2} \\sin\\left({\\theta}\\right)^{2}}{{\\ell}^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\phi}} \\, {\\tau} \\, {\\tau} \\, {\\phi} }^{ \\, {\\phi} \\phantom{\\, {\\tau}} \\phantom{\\, {\\tau}} \\phantom{\\, {\\phi}} } & = & -\\frac{R^{2} + {\\ell}^{2}}{{\\ell}^{4}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\phi}} \\, R \\, R \\, {\\phi} }^{ \\, {\\phi} \\phantom{\\, R} \\phantom{\\, R} \\phantom{\\, {\\phi}} } & = & \\frac{1}{R^{2} + {\\ell}^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\phi}} \\, {\\theta} \\, {\\theta} \\, {\\phi} }^{ \\, {\\phi} \\phantom{\\, {\\theta}} \\phantom{\\, {\\theta}} \\phantom{\\, {\\phi}} } & = & \\frac{R^{2}}{{\\ell}^{2}} \\end{array}$$" ], "text/plain": [ "Riem(g)^ta_R,ta,R = -1/(R^2 + l^2) \n", "Riem(g)^ta_th,ta,th = -R^2/l^2 \n", "Riem(g)^ta_ph,ta,ph = -R^2*sin(th)^2/l^2 \n", "Riem(g)^R_ta,ta,R = -(R^2 + l^2)/l^4 \n", "Riem(g)^R_th,R,th = -R^2/l^2 \n", "Riem(g)^R_ph,R,ph = -R^2*sin(th)^2/l^2 \n", "Riem(g)^th_ta,ta,th = -(R^2 + l^2)/l^4 \n", "Riem(g)^th_R,R,th = 1/(R^2 + l^2) \n", "Riem(g)^th_ph,th,ph = -R^2*sin(th)^2/l^2 \n", "Riem(g)^ph_ta,ta,ph = -(R^2 + l^2)/l^4 \n", "Riem(g)^ph_R,R,ph = 1/(R^2 + l^2) \n", "Riem(g)^ph_th,th,ph = R^2/l^2 " ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Riem.display_comp(X_stat.frame(), X_stat, only_nonredundant=True)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} \\Phi:& \\mathcal{M} & \\longrightarrow & \\mathbb{R}^{2,3} \\\\ \\mbox{on}\\ \\mathcal{M}_0 : & \\left({\\tau}, R, {\\theta}, {\\phi}\\right) & \\longmapsto & \\left(U, V, X, Y, Z\\right) = \\left(\\sqrt{R^{2} + {\\ell}^{2}} \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right), \\sqrt{R^{2} + {\\ell}^{2}} \\sin\\left(\\frac{{\\tau}}{{\\ell}}\\right), R \\cos\\left({\\phi}\\right) \\sin\\left({\\theta}\\right), R \\sin\\left({\\phi}\\right) \\sin\\left({\\theta}\\right), R \\cos\\left({\\theta}\\right)\\right) \\end{array}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} \\Phi:& \\mathcal{M} & \\longrightarrow & \\mathbb{R}^{2,3} \\\\ \\mbox{on}\\ \\mathcal{M}_0 : & \\left({\\tau}, R, {\\theta}, {\\phi}\\right) & \\longmapsto & \\left(U, V, X, Y, Z\\right) = \\left(\\sqrt{R^{2} + {\\ell}^{2}} \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right), \\sqrt{R^{2} + {\\ell}^{2}} \\sin\\left(\\frac{{\\tau}}{{\\ell}}\\right), R \\cos\\left({\\phi}\\right) \\sin\\left({\\theta}\\right), R \\sin\\left({\\phi}\\right) \\sin\\left({\\theta}\\right), R \\cos\\left({\\theta}\\right)\\right) \\end{array}$$" ], "text/plain": [ "Phi: M → R23\n", "on M_0: (ta, R, th, ph) ↦ (U, V, X, Y, Z) = (sqrt(R^2 + l^2)*cos(ta/l), sqrt(R^2 + l^2)*sin(ta/l), R*cos(ph)*sin(th), R*sin(ph)*sin(th), R*cos(th))" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Phi.display(X_stat, X23)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A view of the various geodesics in terms of the coordinates $(\\tau,R)$:" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "Graphics object consisting of 39 graphics primitives" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "graph = Graphics()\n", "for geod in null_geods:\n", " geod.expr(geod.domain().canonical_chart(), X_stat)\n", " graph += geod.plot(X_stat, ambient_coords=(R,ta), prange=(-3,3),\n", " parameters={l:1}, color='green', thickness=1.5)\n", "graph += X_stat.plot(X_stat, ambient_coords=(R,ta), fixed_coords={th:0, ph:pi}, \n", " ranges={ta:(-pi,pi), R:(0,5)}, \n", " number_values={ta: 9, R: 11},\n", " color={ta:'grey', R:'grey'}, parameters={l:1})\n", "time_geod.expr(time_geod.domain().canonical_chart(), X_stat)\n", "graph += time_geod.plot(X_stat, ambient_coords=(R,ta), plot_points=800,\n", " parameters={l:1}, color='purple', thickness=2)\n", "show(graph, aspect_ratio=1, ymin=-pi, ymax=pi, xmin=0, xmax=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conformal coordinates\n", "\n", "We introduce coordinates $(\\tilde{\\tau},\\chi,\\theta,\\phi)$ such that \n", "$$ \\tilde{\\tau} = \\frac{\\tau}{\\ell} \\qquad\\mbox{and}\\qquad \\chi = \\mathrm{atan}\\left(\\frac{R}{\\ell}\\right) $$" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Chart (M_0, (tat, ch, th, ph))\n" ] }, { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left(\\mathcal{M}_0,({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi})\\right)\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left(\\mathcal{M}_0,({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi})\\right)$$" ], "text/plain": [ "Chart (M_0, (tat, ch, th, ph))" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_conf. = M0.chart(r'tat:\\tilde{\\tau} ch:(0,pi/2):\\chi th:(0,pi):\\theta ph:(0,2*pi):\\phi')\n", "print(X_conf); X_conf" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}{\\tilde{\\tau}} :\\ \\left( -\\infty, +\\infty \\right) ;\\quad {\\chi} :\\ \\left( 0 , \\frac{1}{2} \\, \\pi \\right) ;\\quad {\\theta} :\\ \\left( 0 , \\pi \\right) ;\\quad {\\phi} :\\ \\left( 0 , 2 \\, \\pi \\right)\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}{\\tilde{\\tau}} :\\ \\left( -\\infty, +\\infty \\right) ;\\quad {\\chi} :\\ \\left( 0 , \\frac{1}{2} \\, \\pi \\right) ;\\quad {\\theta} :\\ \\left( 0 , \\pi \\right) ;\\quad {\\phi} :\\ \\left( 0 , 2 \\, \\pi \\right)$$" ], "text/plain": [ "tat: (-oo, +oo); ch: (0, 1/2*pi); th: (0, pi); ph: (0, 2*pi)" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_conf.coord_range()" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left\\{\\begin{array}{lcl} {\\tilde{\\tau}} & = & \\frac{{\\tau}}{{\\ell}} \\\\ {\\chi} & = & \\arctan\\left(\\frac{R}{{\\ell}}\\right) \\\\ {\\theta} & = & {\\theta} \\\\ {\\phi} & = & {\\phi} \\end{array}\\right.\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left\\{\\begin{array}{lcl} {\\tilde{\\tau}} & = & \\frac{{\\tau}}{{\\ell}} \\\\ {\\chi} & = & \\arctan\\left(\\frac{R}{{\\ell}}\\right) \\\\ {\\theta} & = & {\\theta} \\\\ {\\phi} & = & {\\phi} \\end{array}\\right.$$" ], "text/plain": [ "tat = ta/l\n", "ch = arctan(R/l)\n", "th = th\n", "ph = ph" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stat_to_conf = X_stat.transition_map(X_conf, [ta/l, atan(R/l), th, ph])\n", "stat_to_conf.display()" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left\\{\\begin{array}{lcl} {\\tau} & = & {\\ell} {\\tilde{\\tau}} \\\\ R & = & \\frac{{\\ell} \\sin\\left({\\chi}\\right)}{\\cos\\left({\\chi}\\right)} \\\\ {\\theta} & = & {\\theta} \\\\ {\\phi} & = & {\\phi} \\end{array}\\right.\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left\\{\\begin{array}{lcl} {\\tau} & = & {\\ell} {\\tilde{\\tau}} \\\\ R & = & \\frac{{\\ell} \\sin\\left({\\chi}\\right)}{\\cos\\left({\\chi}\\right)} \\\\ {\\theta} & = & {\\theta} \\\\ {\\phi} & = & {\\phi} \\end{array}\\right.$$" ], "text/plain": [ "ta = l*tat\n", "R = l*sin(ch)/cos(ch)\n", "th = th\n", "ph = ph" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stat_to_conf.inverse().display()" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left\\{\\begin{array}{lcl} {\\tilde{\\tau}} & = & \\frac{{\\tau}}{{\\ell}} \\\\ {\\chi} & = & \\arctan\\left(\\sinh\\left({\\rho}\\right)\\right) \\\\ {\\theta} & = & {\\theta} \\\\ {\\phi} & = & {\\phi} \\end{array}\\right.\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left\\{\\begin{array}{lcl} {\\tilde{\\tau}} & = & \\frac{{\\tau}}{{\\ell}} \\\\ {\\chi} & = & \\arctan\\left(\\sinh\\left({\\rho}\\right)\\right) \\\\ {\\theta} & = & {\\theta} \\\\ {\\phi} & = & {\\phi} \\end{array}\\right.$$" ], "text/plain": [ "tat = ta/l\n", "ch = arctan(sinh(rh))\n", "th = th\n", "ph = ph" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hyp_to_conf = stat_to_conf * hyp_to_stat\n", "hyp_to_conf.display()" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left\\{\\begin{array}{lcl} {\\tau} & = & {\\ell} {\\tilde{\\tau}} \\\\ {\\rho} & = & \\operatorname{arsinh}\\left(\\frac{\\sin\\left({\\chi}\\right)}{\\cos\\left({\\chi}\\right)}\\right) \\\\ {\\theta} & = & {\\theta} \\\\ {\\phi} & = & {\\phi} \\end{array}\\right.\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left\\{\\begin{array}{lcl} {\\tau} & = & {\\ell} {\\tilde{\\tau}} \\\\ {\\rho} & = & \\operatorname{arsinh}\\left(\\frac{\\sin\\left({\\chi}\\right)}{\\cos\\left({\\chi}\\right)}\\right) \\\\ {\\theta} & = & {\\theta} \\\\ {\\phi} & = & {\\phi} \\end{array}\\right.$$" ], "text/plain": [ "ta = l*tat\n", "rh = arcsinh(sin(ch)/cos(ch))\n", "th = th\n", "ph = ph" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "conf_to_hyp = hyp_to_stat.inverse() * stat_to_conf.inverse()\n", "conf_to_hyp.display()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The expression of the metric tensor in the conformal coordinates is" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}g = -\\frac{{\\ell}^{2}}{\\cos\\left({\\chi}\\right)^{2}} \\mathrm{d} {\\tilde{\\tau}}\\otimes \\mathrm{d} {\\tilde{\\tau}} + \\frac{{\\ell}^{2}}{\\cos\\left({\\chi}\\right)^{2}} \\mathrm{d} {\\chi}\\otimes \\mathrm{d} {\\chi} + \\frac{{\\ell}^{2} \\sin\\left({\\chi}\\right)^{2}}{\\cos\\left({\\chi}\\right)^{2}} \\mathrm{d} {\\theta}\\otimes \\mathrm{d} {\\theta} + \\frac{{\\ell}^{2} \\sin\\left({\\chi}\\right)^{2} \\sin\\left({\\theta}\\right)^{2}}{\\cos\\left({\\chi}\\right)^{2}} \\mathrm{d} {\\phi}\\otimes \\mathrm{d} {\\phi}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}g = -\\frac{{\\ell}^{2}}{\\cos\\left({\\chi}\\right)^{2}} \\mathrm{d} {\\tilde{\\tau}}\\otimes \\mathrm{d} {\\tilde{\\tau}} + \\frac{{\\ell}^{2}}{\\cos\\left({\\chi}\\right)^{2}} \\mathrm{d} {\\chi}\\otimes \\mathrm{d} {\\chi} + \\frac{{\\ell}^{2} \\sin\\left({\\chi}\\right)^{2}}{\\cos\\left({\\chi}\\right)^{2}} \\mathrm{d} {\\theta}\\otimes \\mathrm{d} {\\theta} + \\frac{{\\ell}^{2} \\sin\\left({\\chi}\\right)^{2} \\sin\\left({\\theta}\\right)^{2}}{\\cos\\left({\\chi}\\right)^{2}} \\mathrm{d} {\\phi}\\otimes \\mathrm{d} {\\phi}$$" ], "text/plain": [ "g = -l^2/cos(ch)^2 dtat⊗dtat + l^2/cos(ch)^2 dch⊗dch + l^2*sin(ch)^2/cos(ch)^2 dth⊗dth + l^2*sin(ch)^2*sin(th)^2/cos(ch)^2 dph⊗dph" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g.display(X_conf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The immersion of $\\mathcal{M}$ in $(\\mathbb{R}^{2,3},h)$ in terms of the conformal coordinates:" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} \\Phi:& \\mathcal{M} & \\longrightarrow & \\mathbb{R}^{2,3} \\\\ \\mbox{on}\\ \\mathcal{M}_0 : & \\left({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi}\\right) & \\longmapsto & \\left(U, V, X, Y, Z\\right) = \\left(\\frac{{\\ell} \\cos\\left({\\tilde{\\tau}}\\right)}{\\cos\\left({\\chi}\\right)}, \\frac{{\\ell} \\sin\\left({\\tilde{\\tau}}\\right)}{\\cos\\left({\\chi}\\right)}, \\frac{{\\ell} \\cos\\left({\\phi}\\right) \\sin\\left({\\chi}\\right) \\sin\\left({\\theta}\\right)}{\\cos\\left({\\chi}\\right)}, \\frac{{\\ell} \\sin\\left({\\chi}\\right) \\sin\\left({\\phi}\\right) \\sin\\left({\\theta}\\right)}{\\cos\\left({\\chi}\\right)}, \\frac{{\\ell} \\cos\\left({\\theta}\\right) \\sin\\left({\\chi}\\right)}{\\cos\\left({\\chi}\\right)}\\right) \\end{array}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} \\Phi:& \\mathcal{M} & \\longrightarrow & \\mathbb{R}^{2,3} \\\\ \\mbox{on}\\ \\mathcal{M}_0 : & \\left({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi}\\right) & \\longmapsto & \\left(U, V, X, Y, Z\\right) = \\left(\\frac{{\\ell} \\cos\\left({\\tilde{\\tau}}\\right)}{\\cos\\left({\\chi}\\right)}, \\frac{{\\ell} \\sin\\left({\\tilde{\\tau}}\\right)}{\\cos\\left({\\chi}\\right)}, \\frac{{\\ell} \\cos\\left({\\phi}\\right) \\sin\\left({\\chi}\\right) \\sin\\left({\\theta}\\right)}{\\cos\\left({\\chi}\\right)}, \\frac{{\\ell} \\sin\\left({\\chi}\\right) \\sin\\left({\\phi}\\right) \\sin\\left({\\theta}\\right)}{\\cos\\left({\\chi}\\right)}, \\frac{{\\ell} \\cos\\left({\\theta}\\right) \\sin\\left({\\chi}\\right)}{\\cos\\left({\\chi}\\right)}\\right) \\end{array}$$" ], "text/plain": [ "Phi: M → R23\n", "on M_0: (tat, ch, th, ph) ↦ (U, V, X, Y, Z) = (l*cos(tat)/cos(ch), l*sin(tat)/cos(ch), l*cos(ph)*sin(ch)*sin(th)/cos(ch), l*sin(ch)*sin(ph)*sin(th)/cos(ch), l*cos(th)*sin(ch)/cos(ch))" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Phi.display(X_conf, X23)" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{lcl} \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\tilde{\\tau}}} \\, {\\chi} \\, {\\tilde{\\tau}} \\, {\\chi} }^{ \\, {\\tilde{\\tau}} \\phantom{\\, {\\chi}} \\phantom{\\, {\\tilde{\\tau}}} \\phantom{\\, {\\chi}} } & = & -\\frac{1}{\\cos\\left({\\chi}\\right)^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\tilde{\\tau}}} \\, {\\theta} \\, {\\tilde{\\tau}} \\, {\\theta} }^{ \\, {\\tilde{\\tau}} \\phantom{\\, {\\theta}} \\phantom{\\, {\\tilde{\\tau}}} \\phantom{\\, {\\theta}} } & = & -\\frac{\\sin\\left({\\chi}\\right)^{2}}{\\cos\\left({\\chi}\\right)^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\tilde{\\tau}}} \\, {\\phi} \\, {\\tilde{\\tau}} \\, {\\phi} }^{ \\, {\\tilde{\\tau}} \\phantom{\\, {\\phi}} \\phantom{\\, {\\tilde{\\tau}}} \\phantom{\\, {\\phi}} } & = & -\\frac{\\sin\\left({\\chi}\\right)^{2} \\sin\\left({\\theta}\\right)^{2}}{\\cos\\left({\\chi}\\right)^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\chi}} \\, {\\tilde{\\tau}} \\, {\\tilde{\\tau}} \\, {\\chi} }^{ \\, {\\chi} \\phantom{\\, {\\tilde{\\tau}}} \\phantom{\\, {\\tilde{\\tau}}} \\phantom{\\, {\\chi}} } & = & -\\frac{1}{\\cos\\left({\\chi}\\right)^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\chi}} \\, {\\theta} \\, {\\chi} \\, {\\theta} }^{ \\, {\\chi} \\phantom{\\, {\\theta}} \\phantom{\\, {\\chi}} \\phantom{\\, {\\theta}} } & = & -\\frac{\\sin\\left({\\chi}\\right)^{2}}{\\cos\\left({\\chi}\\right)^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\chi}} \\, {\\phi} \\, {\\chi} \\, {\\phi} }^{ \\, {\\chi} \\phantom{\\, {\\phi}} \\phantom{\\, {\\chi}} \\phantom{\\, {\\phi}} } & = & -\\frac{\\sin\\left({\\chi}\\right)^{2} \\sin\\left({\\theta}\\right)^{2}}{\\cos\\left({\\chi}\\right)^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\theta}} \\, {\\tilde{\\tau}} \\, {\\tilde{\\tau}} \\, {\\theta} }^{ \\, {\\theta} \\phantom{\\, {\\tilde{\\tau}}} \\phantom{\\, {\\tilde{\\tau}}} \\phantom{\\, {\\theta}} } & = & -\\frac{1}{\\cos\\left({\\chi}\\right)^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\theta}} \\, {\\chi} \\, {\\chi} \\, {\\theta} }^{ \\, {\\theta} \\phantom{\\, {\\chi}} \\phantom{\\, {\\chi}} \\phantom{\\, {\\theta}} } & = & \\frac{1}{\\cos\\left({\\chi}\\right)^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\theta}} \\, {\\phi} \\, {\\theta} \\, {\\phi} }^{ \\, {\\theta} \\phantom{\\, {\\phi}} \\phantom{\\, {\\theta}} \\phantom{\\, {\\phi}} } & = & -\\frac{\\sin\\left({\\chi}\\right)^{2} \\sin\\left({\\theta}\\right)^{2}}{\\cos\\left({\\chi}\\right)^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\phi}} \\, {\\tilde{\\tau}} \\, {\\tilde{\\tau}} \\, {\\phi} }^{ \\, {\\phi} \\phantom{\\, {\\tilde{\\tau}}} \\phantom{\\, {\\tilde{\\tau}}} \\phantom{\\, {\\phi}} } & = & -\\frac{1}{\\cos\\left({\\chi}\\right)^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\phi}} \\, {\\chi} \\, {\\chi} \\, {\\phi} }^{ \\, {\\phi} \\phantom{\\, {\\chi}} \\phantom{\\, {\\chi}} \\phantom{\\, {\\phi}} } & = & \\frac{1}{\\cos\\left({\\chi}\\right)^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\phi}} \\, {\\theta} \\, {\\theta} \\, {\\phi} }^{ \\, {\\phi} \\phantom{\\, {\\theta}} \\phantom{\\, {\\theta}} \\phantom{\\, {\\phi}} } & = & \\frac{\\sin\\left({\\chi}\\right)^{2}}{\\cos\\left({\\chi}\\right)^{2}} \\end{array}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{lcl} \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\tilde{\\tau}}} \\, {\\chi} \\, {\\tilde{\\tau}} \\, {\\chi} }^{ \\, {\\tilde{\\tau}} \\phantom{\\, {\\chi}} \\phantom{\\, {\\tilde{\\tau}}} \\phantom{\\, {\\chi}} } & = & -\\frac{1}{\\cos\\left({\\chi}\\right)^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\tilde{\\tau}}} \\, {\\theta} \\, {\\tilde{\\tau}} \\, {\\theta} }^{ \\, {\\tilde{\\tau}} \\phantom{\\, {\\theta}} \\phantom{\\, {\\tilde{\\tau}}} \\phantom{\\, {\\theta}} } & = & -\\frac{\\sin\\left({\\chi}\\right)^{2}}{\\cos\\left({\\chi}\\right)^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\tilde{\\tau}}} \\, {\\phi} \\, {\\tilde{\\tau}} \\, {\\phi} }^{ \\, {\\tilde{\\tau}} \\phantom{\\, {\\phi}} \\phantom{\\, {\\tilde{\\tau}}} \\phantom{\\, {\\phi}} } & = & -\\frac{\\sin\\left({\\chi}\\right)^{2} \\sin\\left({\\theta}\\right)^{2}}{\\cos\\left({\\chi}\\right)^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\chi}} \\, {\\tilde{\\tau}} \\, {\\tilde{\\tau}} \\, {\\chi} }^{ \\, {\\chi} \\phantom{\\, {\\tilde{\\tau}}} \\phantom{\\, {\\tilde{\\tau}}} \\phantom{\\, {\\chi}} } & = & -\\frac{1}{\\cos\\left({\\chi}\\right)^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\chi}} \\, {\\theta} \\, {\\chi} \\, {\\theta} }^{ \\, {\\chi} \\phantom{\\, {\\theta}} \\phantom{\\, {\\chi}} \\phantom{\\, {\\theta}} } & = & -\\frac{\\sin\\left({\\chi}\\right)^{2}}{\\cos\\left({\\chi}\\right)^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\chi}} \\, {\\phi} \\, {\\chi} \\, {\\phi} }^{ \\, {\\chi} \\phantom{\\, {\\phi}} \\phantom{\\, {\\chi}} \\phantom{\\, {\\phi}} } & = & -\\frac{\\sin\\left({\\chi}\\right)^{2} \\sin\\left({\\theta}\\right)^{2}}{\\cos\\left({\\chi}\\right)^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\theta}} \\, {\\tilde{\\tau}} \\, {\\tilde{\\tau}} \\, {\\theta} }^{ \\, {\\theta} \\phantom{\\, {\\tilde{\\tau}}} \\phantom{\\, {\\tilde{\\tau}}} \\phantom{\\, {\\theta}} } & = & -\\frac{1}{\\cos\\left({\\chi}\\right)^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\theta}} \\, {\\chi} \\, {\\chi} \\, {\\theta} }^{ \\, {\\theta} \\phantom{\\, {\\chi}} \\phantom{\\, {\\chi}} \\phantom{\\, {\\theta}} } & = & \\frac{1}{\\cos\\left({\\chi}\\right)^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\theta}} \\, {\\phi} \\, {\\theta} \\, {\\phi} }^{ \\, {\\theta} \\phantom{\\, {\\phi}} \\phantom{\\, {\\theta}} \\phantom{\\, {\\phi}} } & = & -\\frac{\\sin\\left({\\chi}\\right)^{2} \\sin\\left({\\theta}\\right)^{2}}{\\cos\\left({\\chi}\\right)^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\phi}} \\, {\\tilde{\\tau}} \\, {\\tilde{\\tau}} \\, {\\phi} }^{ \\, {\\phi} \\phantom{\\, {\\tilde{\\tau}}} \\phantom{\\, {\\tilde{\\tau}}} \\phantom{\\, {\\phi}} } & = & -\\frac{1}{\\cos\\left({\\chi}\\right)^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\phi}} \\, {\\chi} \\, {\\chi} \\, {\\phi} }^{ \\, {\\phi} \\phantom{\\, {\\chi}} \\phantom{\\, {\\chi}} \\phantom{\\, {\\phi}} } & = & \\frac{1}{\\cos\\left({\\chi}\\right)^{2}} \\\\ \\mathrm{Riem}\\left(g\\right)_{ \\phantom{\\, {\\phi}} \\, {\\theta} \\, {\\theta} \\, {\\phi} }^{ \\, {\\phi} \\phantom{\\, {\\theta}} \\phantom{\\, {\\theta}} \\phantom{\\, {\\phi}} } & = & \\frac{\\sin\\left({\\chi}\\right)^{2}}{\\cos\\left({\\chi}\\right)^{2}} \\end{array}$$" ], "text/plain": [ "Riem(g)^tat_ch,tat,ch = -1/cos(ch)^2 \n", "Riem(g)^tat_th,tat,th = -sin(ch)^2/cos(ch)^2 \n", "Riem(g)^tat_ph,tat,ph = -sin(ch)^2*sin(th)^2/cos(ch)^2 \n", "Riem(g)^ch_tat,tat,ch = -1/cos(ch)^2 \n", "Riem(g)^ch_th,ch,th = -sin(ch)^2/cos(ch)^2 \n", "Riem(g)^ch_ph,ch,ph = -sin(ch)^2*sin(th)^2/cos(ch)^2 \n", "Riem(g)^th_tat,tat,th = -1/cos(ch)^2 \n", "Riem(g)^th_ch,ch,th = cos(ch)^(-2) \n", "Riem(g)^th_ph,th,ph = -sin(ch)^2*sin(th)^2/cos(ch)^2 \n", "Riem(g)^ph_tat,tat,ph = -1/cos(ch)^2 \n", "Riem(g)^ph_ch,ch,ph = cos(ch)^(-2) \n", "Riem(g)^ph_th,th,ph = sin(ch)^2/cos(ch)^2 " ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Riem.display_comp(X_conf.frame(), X_conf, only_nonredundant=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us draw the grid of hyperbolic coordinates in terms of the conformal ones:" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "Graphics object consisting of 29 graphics primitives" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "graph = X_hyp.plot(X_conf, ambient_coords=(ch, tat), fixed_coords={th: pi/2, ph: pi}, \n", " ranges={ta: (-pi,pi), rh: (0,10)}, number_values={ta: 9, rh: 20},\n", " parameters={l:1}, color={ta: 'red', rh: 'grey'})\n", "show(graph, aspect_ratio=0.25)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Same thing for the grid of static coordinates in terms of the conformal ones:" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "Graphics object consisting of 49 graphics primitives" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "graph = X_stat.plot(X_conf, ambient_coords=(ch, tat), fixed_coords={th: pi/2, ph: pi}, \n", " ranges={ta: (-pi,pi), R: (0,40)}, number_values={ta: 9, R: 40},\n", " parameters={l:1}, color={ta: 'red', R: 'grey'})\n", "show(graph, aspect_ratio=0.25)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us add some geodesics:" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} & \\Bold{R} & \\longrightarrow & \\mathcal{M} \\\\ & {\\tau} & \\longmapsto & \\left({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi}\\right) = \\left(\\frac{{\\tau}}{{\\ell}}, \\arctan\\left(\\sinh\\left({\\left| \\operatorname{artanh}\\left(\\frac{4}{5} \\, \\sin\\left(\\frac{{\\tau}}{{\\ell}}\\right)\\right) \\right|}\\right)\\right), \\frac{1}{2} \\, \\pi, \\pi \\mathrm{u}\\left(\\operatorname{frac}\\left(\\frac{{\\tau}}{2 \\, \\pi {\\ell}}\\right) - \\frac{1}{2}\\right)\\right) \\end{array}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} & \\Bold{R} & \\longrightarrow & \\mathcal{M} \\\\ & {\\tau} & \\longmapsto & \\left({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi}\\right) = \\left(\\frac{{\\tau}}{{\\ell}}, \\arctan\\left(\\sinh\\left({\\left| \\operatorname{artanh}\\left(\\frac{4}{5} \\, \\sin\\left(\\frac{{\\tau}}{{\\ell}}\\right)\\right) \\right|}\\right)\\right), \\frac{1}{2} \\, \\pi, \\pi \\mathrm{u}\\left(\\operatorname{frac}\\left(\\frac{{\\tau}}{2 \\, \\pi {\\ell}}\\right) - \\frac{1}{2}\\right)\\right) \\end{array}$$" ], "text/plain": [ "ℝ → M\n", " ta ↦ (tat, ch, th, ph) = (ta/l, arctan(sinh(abs(arctanh(4/5*sin(ta/l))))), 1/2*pi, pi*unit_step(frac(1/2*ta/(pi*l)) - 1/2))" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "for geod in null_geods:\n", " geod.display(geod.domain().canonical_chart(), X_conf)\n", "time_geod.display(time_geod.domain().canonical_chart(), X_conf)" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "Graphics object consisting of 68 graphics primitives" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for geod in null_geods:\n", " graph += geod.plot(X_conf, ambient_coords=(ch,tat), \n", " parameters={l:1}, color='green', thickness=2)\n", "graph += time_geod.plot(X_conf, ambient_coords=(ch,tat), prange=(-pi, pi),\n", " plot_points=200, parameters={l:1}, color='purple', thickness=2)\n", "show(graph, aspect_ratio=0.25, ymin=-pi, ymax=pi)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We notice that the null geodesics are straight lines in terms of the conformal coordinates\n", "$(\\tau,\\chi)$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conformal metric" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us call $\\Omega^{-2}$ the common factor that appear in the expression of the metric in conformal coordinates: " ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} \\Omega:& \\mathcal{M} & \\longrightarrow & \\mathbb{R} \\\\ \\mbox{on}\\ \\mathcal{M}_0 : & \\left({\\tau}, {\\rho}, {\\theta}, {\\phi}\\right) & \\longmapsto & \\frac{1}{{\\ell} \\cosh\\left({\\rho}\\right)} \\\\ \\mbox{on}\\ \\mathcal{M}_0 : & \\left({\\tau}, R, {\\theta}, {\\phi}\\right) & \\longmapsto & \\frac{1}{\\sqrt{R^{2} + {\\ell}^{2}}} \\\\ \\mbox{on}\\ \\mathcal{M}_0 : & \\left({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi}\\right) & \\longmapsto & \\frac{\\cos\\left({\\chi}\\right)}{{\\ell}} \\end{array}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} \\Omega:& \\mathcal{M} & \\longrightarrow & \\mathbb{R} \\\\ \\mbox{on}\\ \\mathcal{M}_0 : & \\left({\\tau}, {\\rho}, {\\theta}, {\\phi}\\right) & \\longmapsto & \\frac{1}{{\\ell} \\cosh\\left({\\rho}\\right)} \\\\ \\mbox{on}\\ \\mathcal{M}_0 : & \\left({\\tau}, R, {\\theta}, {\\phi}\\right) & \\longmapsto & \\frac{1}{\\sqrt{R^{2} + {\\ell}^{2}}} \\\\ \\mbox{on}\\ \\mathcal{M}_0 : & \\left({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi}\\right) & \\longmapsto & \\frac{\\cos\\left({\\chi}\\right)}{{\\ell}} \\end{array}$$" ], "text/plain": [ "Omega: M → ℝ\n", "on M_0: (ta, rh, th, ph) ↦ 1/(l*cosh(rh))\n", "on M_0: (ta, R, th, ph) ↦ 1/sqrt(R^2 + l^2)\n", "on M_0: (tat, ch, th, ph) ↦ cos(ch)/l" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Omega = M.scalar_field({X_conf: cos(ch)/l}, name='Omega', latex_name=r'\\Omega')\n", "Omega.display()" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} \\Omega:& \\mathcal{M} & \\longrightarrow & \\mathbb{R} \\\\ \\mbox{on}\\ \\mathcal{M}_0 : & \\left({\\tau}, {\\rho}, {\\theta}, {\\phi}\\right) & \\longmapsto & \\frac{1}{{\\ell} \\cosh\\left({\\rho}\\right)} \\end{array}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} \\Omega:& \\mathcal{M} & \\longrightarrow & \\mathbb{R} \\\\ \\mbox{on}\\ \\mathcal{M}_0 : & \\left({\\tau}, {\\rho}, {\\theta}, {\\phi}\\right) & \\longmapsto & \\frac{1}{{\\ell} \\cosh\\left({\\rho}\\right)} \\end{array}$$" ], "text/plain": [ "Omega: M → ℝ\n", "on M_0: (ta, rh, th, ph) ↦ 1/(l*cosh(rh))" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Omega.display(X_hyp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We introduce the metric $\\tilde g = \\Omega^2 g$:" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\tilde{g} = -\\mathrm{d} {\\tilde{\\tau}}\\otimes \\mathrm{d} {\\tilde{\\tau}}+\\mathrm{d} {\\chi}\\otimes \\mathrm{d} {\\chi} + \\sin\\left({\\chi}\\right)^{2} \\mathrm{d} {\\theta}\\otimes \\mathrm{d} {\\theta} + \\sin\\left({\\chi}\\right)^{2} \\sin\\left({\\theta}\\right)^{2} \\mathrm{d} {\\phi}\\otimes \\mathrm{d} {\\phi}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\tilde{g} = -\\mathrm{d} {\\tilde{\\tau}}\\otimes \\mathrm{d} {\\tilde{\\tau}}+\\mathrm{d} {\\chi}\\otimes \\mathrm{d} {\\chi} + \\sin\\left({\\chi}\\right)^{2} \\mathrm{d} {\\theta}\\otimes \\mathrm{d} {\\theta} + \\sin\\left({\\chi}\\right)^{2} \\sin\\left({\\theta}\\right)^{2} \\mathrm{d} {\\phi}\\otimes \\mathrm{d} {\\phi}$$" ], "text/plain": [ "gt = -dtat⊗dtat + dch⊗dch + sin(ch)^2 dth⊗dth + sin(ch)^2*sin(th)^2 dph⊗dph" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gt = M.lorentzian_metric('gt', latex_name=r'\\tilde{g}')\n", "gt.set(Omega^2*g)\n", "gt.display(X_conf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Einstein static universe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Einstein static universe is the manifold $\\mathbb{R}\\times\\mathbb{S}^3$ equipped with a Lorentzian metric equivalent to $\\tilde{g}$. We consider here the part $E$ of $\\mathbb{R}\\times\\mathbb{S}^3$ covered by hyperspherical coordinates:" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4-dimensional differentiable manifold E\n" ] } ], "source": [ "E = Manifold(4, 'E')\n", "print(E)" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left(E,({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi})\\right)\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left(E,({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi})\\right)$$" ], "text/plain": [ "Chart (E, (tat, cht, th, ph))" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "XE. = E.chart(r'tat:\\tilde{\\tau} cht:(0,pi):\\chi th:(0,pi):\\theta ph:(0,2*pi):\\phi')\n", "XE" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}{\\tilde{\\tau}} :\\ \\left( -\\infty, +\\infty \\right) ;\\quad {\\chi} :\\ \\left( 0 , \\pi \\right) ;\\quad {\\theta} :\\ \\left( 0 , \\pi \\right) ;\\quad {\\phi} :\\ \\left( 0 , 2 \\, \\pi \\right)\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}{\\tilde{\\tau}} :\\ \\left( -\\infty, +\\infty \\right) ;\\quad {\\chi} :\\ \\left( 0 , \\pi \\right) ;\\quad {\\theta} :\\ \\left( 0 , \\pi \\right) ;\\quad {\\phi} :\\ \\left( 0 , 2 \\, \\pi \\right)$$" ], "text/plain": [ "tat: (-oo, +oo); cht: (0, pi); th: (0, pi); ph: (0, 2*pi)" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "XE.coord_range()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The conformal completion of AdS spacetime is defined by the map:" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Differentiable map Psi from the 4-dimensional Lorentzian manifold M to the 4-dimensional differentiable manifold E\n" ] }, { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} \\Psi:& \\mathcal{M} & \\longrightarrow & E \\\\ \\mbox{on}\\ \\mathcal{M}_0 : & \\left({\\tau}, {\\rho}, {\\theta}, {\\phi}\\right) & \\longmapsto & \\left({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi}\\right) = \\left(\\frac{{\\tau}}{{\\ell}}, \\arctan\\left(\\sinh\\left({\\rho}\\right)\\right), {\\theta}, {\\phi}\\right) \\\\ \\mbox{on}\\ \\mathcal{M}_0 : & \\left({\\tau}, R, {\\theta}, {\\phi}\\right) & \\longmapsto & \\left({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi}\\right) = \\left(\\frac{{\\tau}}{{\\ell}}, \\arctan\\left(\\frac{R}{{\\ell}}\\right), {\\theta}, {\\phi}\\right) \\\\ \\mbox{on}\\ \\mathcal{M}_0 : & \\left({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi}\\right) & \\longmapsto & \\left({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi}\\right) = \\left({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi}\\right) \\end{array}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} \\Psi:& \\mathcal{M} & \\longrightarrow & E \\\\ \\mbox{on}\\ \\mathcal{M}_0 : & \\left({\\tau}, {\\rho}, {\\theta}, {\\phi}\\right) & \\longmapsto & \\left({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi}\\right) = \\left(\\frac{{\\tau}}{{\\ell}}, \\arctan\\left(\\sinh\\left({\\rho}\\right)\\right), {\\theta}, {\\phi}\\right) \\\\ \\mbox{on}\\ \\mathcal{M}_0 : & \\left({\\tau}, R, {\\theta}, {\\phi}\\right) & \\longmapsto & \\left({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi}\\right) = \\left(\\frac{{\\tau}}{{\\ell}}, \\arctan\\left(\\frac{R}{{\\ell}}\\right), {\\theta}, {\\phi}\\right) \\\\ \\mbox{on}\\ \\mathcal{M}_0 : & \\left({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi}\\right) & \\longmapsto & \\left({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi}\\right) = \\left({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi}\\right) \\end{array}$$" ], "text/plain": [ "Psi: M → E\n", "on M_0: (ta, rh, th, ph) ↦ (tat, cht, th, ph) = (ta/l, arctan(sinh(rh)), th, ph)\n", "on M_0: (ta, R, th, ph) ↦ (tat, cht, th, ph) = (ta/l, arctan(R/l), th, ph)\n", "on M_0: (tat, ch, th, ph) ↦ (tat, cht, th, ph) = (tat, ch, th, ph)" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Psi = M.diff_map(E, {(X_conf, XE): [tat, ch, th, ph]},\n", " name='Psi', latex_name=r'\\Psi')\n", "print(Psi)\n", "Psi.display()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Embedding of $E$ in $\\mathbb{R}^5$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For visualization purposes, we introduce the (differentiable, not isometric) embedding $\\Phi_E$ of the Einstein cylinder $\\mathbb{R}\\times\\mathbb{S}^3$ in $\\mathbb{R}^5$ that follows immediately from the canonical embedding of $\\mathbb{S}^3$ in $\\mathbb{R}^4$. We introduce $\\mathbb{R}^5$ first:" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left(\\mathbb{R}^5,(T, W, X, Y, Z)\\right)\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left(\\mathbb{R}^5,(T, W, X, Y, Z)\\right)$$" ], "text/plain": [ "Chart (R5, (T, W, X, Y, Z))" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "R5 = Manifold(5, 'R5', latex_name=r'\\mathbb{R}^5')\n", "X5. = R5.chart()\n", "X5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and define $\\Phi_E$ as " ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Differentiable map Phi_E from the 4-dimensional differentiable manifold E to the 5-dimensional differentiable manifold R5\n" ] }, { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} \\Phi_E:& E & \\longrightarrow & \\mathbb{R}^5 \\\\ & \\left({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi}\\right) & \\longmapsto & \\left(T, W, X, Y, Z\\right) = \\left({\\tilde{\\tau}}, \\cos\\left({\\chi}\\right), \\cos\\left({\\phi}\\right) \\sin\\left({\\chi}\\right) \\sin\\left({\\theta}\\right), \\sin\\left({\\chi}\\right) \\sin\\left({\\phi}\\right) \\sin\\left({\\theta}\\right), \\cos\\left({\\theta}\\right) \\sin\\left({\\chi}\\right)\\right) \\end{array}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} \\Phi_E:& E & \\longrightarrow & \\mathbb{R}^5 \\\\ & \\left({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi}\\right) & \\longmapsto & \\left(T, W, X, Y, Z\\right) = \\left({\\tilde{\\tau}}, \\cos\\left({\\chi}\\right), \\cos\\left({\\phi}\\right) \\sin\\left({\\chi}\\right) \\sin\\left({\\theta}\\right), \\sin\\left({\\chi}\\right) \\sin\\left({\\phi}\\right) \\sin\\left({\\theta}\\right), \\cos\\left({\\theta}\\right) \\sin\\left({\\chi}\\right)\\right) \\end{array}$$" ], "text/plain": [ "Phi_E: E → R5\n", " (tat, cht, th, ph) ↦ (T, W, X, Y, Z) = (tat, cos(cht), cos(ph)*sin(cht)*sin(th), sin(cht)*sin(ph)*sin(th), cos(th)*sin(cht))" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "PhiE = E.diff_map(R5, {(XE, X5): [tat,\n", " cos(cht),\n", " sin(cht)*sin(th)*cos(ph), \n", " sin(cht)*sin(th)*sin(ph), \n", " sin(cht)*cos(th)]},\n", " name='Phi_E', latex_name=r'\\Phi_E')\n", "print(PhiE)\n", "PhiE.display()" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "Graphics3d Object" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "graphE = XE.plot(X5, ambient_coords=(W,X,T), mapping=PhiE, fixed_coords={th:pi/2, ph:0}, \n", " ranges={tat: (-pi,pi), cht: (0,pi)}, number_values=9, color='silver', \n", " thickness=0.5, label_axes=False) # phi = 0 \n", "graphE += XE.plot(X5, ambient_coords=(W,X,T), mapping=PhiE, fixed_coords={th:pi/2, ph:pi}, \n", " ranges={tat: (-pi,pi), cht: (0,pi)}, number_values=9, color='silver', \n", " thickness=0.5, label_axes=False) # phi = pi\n", "show(graphE, aspect_ratio=1, axes_labels=['W','X','tau'])" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## View of $\\mathrm{AdS}_4$ on the Einstein cylinder\n", "\n", "The view is obtained by composing the embeddings\n", "$\\Psi:\\, \\mathcal{M}\\rightarrow E$ and $\\Phi_E:\\, E\\rightarrow \\mathbb{R}^5$, i.e. by introducing \n", "$$\\Theta= \\Phi_E\\circ \\Psi :\\ \\mathcal{M}\\rightarrow \\mathbb{R}^5$$" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Differentiable map from the 4-dimensional Lorentzian manifold M to the 5-dimensional differentiable manifold R5\n" ] }, { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} & \\mathcal{M} & \\longrightarrow & \\mathbb{R}^5 \\\\ \\mbox{on}\\ \\mathcal{M}_0 : & \\left({\\tau}, {\\rho}, {\\theta}, {\\phi}\\right) & \\longmapsto & \\left(T, W, X, Y, Z\\right) = \\left(\\frac{{\\tau}}{{\\ell}}, \\frac{1}{\\cosh\\left({\\rho}\\right)}, \\frac{\\cos\\left({\\phi}\\right) \\sin\\left({\\theta}\\right) \\sinh\\left({\\rho}\\right)}{\\cosh\\left({\\rho}\\right)}, \\frac{\\sin\\left({\\phi}\\right) \\sin\\left({\\theta}\\right) \\sinh\\left({\\rho}\\right)}{\\cosh\\left({\\rho}\\right)}, \\frac{\\cos\\left({\\theta}\\right) \\sinh\\left({\\rho}\\right)}{\\cosh\\left({\\rho}\\right)}\\right) \\\\ \\mbox{on}\\ \\mathcal{M}_0 : & \\left({\\tau}, R, {\\theta}, {\\phi}\\right) & \\longmapsto & \\left(T, W, X, Y, Z\\right) = \\left(\\frac{{\\tau}}{{\\ell}}, \\frac{{\\ell}}{\\sqrt{R^{2} + {\\ell}^{2}}}, \\frac{R \\cos\\left({\\phi}\\right) \\sin\\left({\\theta}\\right)}{\\sqrt{R^{2} + {\\ell}^{2}}}, \\frac{R \\sin\\left({\\phi}\\right) \\sin\\left({\\theta}\\right)}{\\sqrt{R^{2} + {\\ell}^{2}}}, \\frac{R \\cos\\left({\\theta}\\right)}{\\sqrt{R^{2} + {\\ell}^{2}}}\\right) \\\\ \\mbox{on}\\ \\mathcal{M}_0 : & \\left({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi}\\right) & \\longmapsto & \\left(T, W, X, Y, Z\\right) = \\left({\\tilde{\\tau}}, \\cos\\left({\\chi}\\right), \\cos\\left({\\phi}\\right) \\sin\\left({\\chi}\\right) \\sin\\left({\\theta}\\right), \\sin\\left({\\chi}\\right) \\sin\\left({\\phi}\\right) \\sin\\left({\\theta}\\right), \\cos\\left({\\theta}\\right) \\sin\\left({\\chi}\\right)\\right) \\end{array}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} & \\mathcal{M} & \\longrightarrow & \\mathbb{R}^5 \\\\ \\mbox{on}\\ \\mathcal{M}_0 : & \\left({\\tau}, {\\rho}, {\\theta}, {\\phi}\\right) & \\longmapsto & \\left(T, W, X, Y, Z\\right) = \\left(\\frac{{\\tau}}{{\\ell}}, \\frac{1}{\\cosh\\left({\\rho}\\right)}, \\frac{\\cos\\left({\\phi}\\right) \\sin\\left({\\theta}\\right) \\sinh\\left({\\rho}\\right)}{\\cosh\\left({\\rho}\\right)}, \\frac{\\sin\\left({\\phi}\\right) \\sin\\left({\\theta}\\right) \\sinh\\left({\\rho}\\right)}{\\cosh\\left({\\rho}\\right)}, \\frac{\\cos\\left({\\theta}\\right) \\sinh\\left({\\rho}\\right)}{\\cosh\\left({\\rho}\\right)}\\right) \\\\ \\mbox{on}\\ \\mathcal{M}_0 : & \\left({\\tau}, R, {\\theta}, {\\phi}\\right) & \\longmapsto & \\left(T, W, X, Y, Z\\right) = \\left(\\frac{{\\tau}}{{\\ell}}, \\frac{{\\ell}}{\\sqrt{R^{2} + {\\ell}^{2}}}, \\frac{R \\cos\\left({\\phi}\\right) \\sin\\left({\\theta}\\right)}{\\sqrt{R^{2} + {\\ell}^{2}}}, \\frac{R \\sin\\left({\\phi}\\right) \\sin\\left({\\theta}\\right)}{\\sqrt{R^{2} + {\\ell}^{2}}}, \\frac{R \\cos\\left({\\theta}\\right)}{\\sqrt{R^{2} + {\\ell}^{2}}}\\right) \\\\ \\mbox{on}\\ \\mathcal{M}_0 : & \\left({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi}\\right) & \\longmapsto & \\left(T, W, X, Y, Z\\right) = \\left({\\tilde{\\tau}}, \\cos\\left({\\chi}\\right), \\cos\\left({\\phi}\\right) \\sin\\left({\\chi}\\right) \\sin\\left({\\theta}\\right), \\sin\\left({\\chi}\\right) \\sin\\left({\\phi}\\right) \\sin\\left({\\theta}\\right), \\cos\\left({\\theta}\\right) \\sin\\left({\\chi}\\right)\\right) \\end{array}$$" ], "text/plain": [ "M → R5\n", "on M_0: (ta, rh, th, ph) ↦ (T, W, X, Y, Z) = (ta/l, 1/cosh(rh), cos(ph)*sin(th)*sinh(rh)/cosh(rh), sin(ph)*sin(th)*sinh(rh)/cosh(rh), cos(th)*sinh(rh)/cosh(rh))\n", "on M_0: (ta, R, th, ph) ↦ (T, W, X, Y, Z) = (ta/l, l/sqrt(R^2 + l^2), R*cos(ph)*sin(th)/sqrt(R^2 + l^2), R*sin(ph)*sin(th)/sqrt(R^2 + l^2), R*cos(th)/sqrt(R^2 + l^2))\n", "on M_0: (tat, ch, th, ph) ↦ (T, W, X, Y, Z) = (tat, cos(ch), cos(ph)*sin(ch)*sin(th), sin(ch)*sin(ph)*sin(th), cos(th)*sin(ch))" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Theta = PhiE * Psi\n", "print(Theta)\n", "Theta.display()" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "Graphics3d Object" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "graph = X_stat.plot(X5, ambient_coords=(W,X,T), mapping=Theta,\n", " fixed_coords={th: pi/2, ph: 0}, ranges={ta: (-pi,pi), R: (0,40)}, \n", " number_values={ta: 9, R: 40}, parameters={l:1}, \n", " color={ta: 'red', R: 'grey'}, label_axes=False) # phi = 0 \n", "graph += X_stat.plot(X5, ambient_coords=(W,X,T), mapping=Theta,\n", " fixed_coords={th: pi/2, ph: pi}, ranges={ta: (-pi,pi), R: (0,40)},\n", " number_values={ta: 9, R: 40}, parameters={l:1}, \n", " color={ta: 'red', R: 'grey'}, label_axes=False) # phi = pi \n", "graph += graphE # superposing the plot of the Einstein cylinder\n", "half_cylinder = parametric_plot3d([sin(ph), cos(ph), ta], (ta, -pi, pi), (ph, 0, pi), \n", " color=(1.,1.,0.9))\n", "graph += half_cylinder\n", "show(graph, aspect_ratio=1, axes_labels=['W','X','tau'])" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "Graphics3d Object" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for geod in null_geods:\n", " graph += geod.plot(X5, ambient_coords=(W,X,T), mapping=Theta,\n", " parameters={l:1}, color='green', thickness=1, label_axes=False)\n", "graph += time_geod.plot(X5, ambient_coords=(W,X,T), mapping=Theta, prange=(-pi, pi),\n", " plot_points=100, parameters={l:1}, color='purple', thickness=2,\n", " label_axes=False)\n", "show(graph, aspect_ratio=1, zmin=-pi, zmax=pi, axes_labels=['W','X','tau'])" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "Graphics3d Object" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "graph1 = graph.rotate((0,0,1), 0.4)\n", "show(graph1, aspect_ratio=1, viewer='tachyon', frame=False, \n", " figsize=20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A different cylindrical view of AdS spacetime is obtained by using the coordinates $(T,X,Y)$ of $\\mathbb{R}^5$ instead of $(T,W,X)$ as above. Let us draw the grid of conformal \n", "coordinates at $\\theta=\\pi/2$, with \n", "- red lines as those along which $\\tilde{\\tau}$ varies at fixed $(\\chi,\\phi)$\n", "- grey lines as those along which $\\chi$ varies at fixed $(\\tilde{\\tau},\\phi)$\n", "- orange lines as those along which $\\phi$ varies at fixed $(\\tilde{\\tau},\\chi)$" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "Graphics3d Object" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "graph = X_conf.plot(X5, ambient_coords=(X,Y,T), mapping=Theta,\n", " fixed_coords={th: pi/2}, \n", " ranges={tat: (-pi,pi), ch: (0,pi/2), ph:(0,2*pi)}, \n", " number_values=6, parameters={l:1}, \n", " color={tat: 'red', ch: 'grey', ph: 'orange'}, \n", " label_axes=False)\n", "graph += graphE\n", "show(graph, aspect_ratio=1, axes_labels=['X','Y','tau'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us add the previously considered null (green) and timelike (purple) geodesics, noticing that they all lie in the plane $Y=0$ for they have $\\phi=0$ or $\\pi$:" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "Graphics3d Object" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for geod in null_geods:\n", " graph += geod.plot(X5, ambient_coords=(X,Y,T), mapping=Theta,\n", " parameters={l:1}, color='green', thickness=1, label_axes=False)\n", "graph += time_geod.plot(X5, ambient_coords=(X,Y,T), mapping=Theta, prange=(-pi, pi),\n", " plot_points=100, parameters={l:1}, color='purple', thickness=2,\n", " label_axes=False)\n", "show(graph, aspect_ratio=1, axes_labels=['X','Y','tau'])" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Poincaré coordinates" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Poincaré coordinates are defined on the open subset $\\mathcal{M}_{\\rm P}$ of $\\mathcal{M}_0$ defined by $U-X>0$ in terms of the canonical immersion $\\Phi$ in $\\mathbb{R}^{2,3}$. The open subset $\\mathcal{M}_{\\rm P}$ is usually called the **Poincaré patch** of AdS spacetime and its boundary $U-X=0$ is called the **Poincaré horizon**.\n", "\n", "Given the expression of $\\Phi$:" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} \\Phi:& \\mathcal{M} & \\longrightarrow & \\mathbb{R}^{2,3} \\\\ \\mbox{on}\\ \\mathcal{M}_0 : & \\left({\\tau}, {\\rho}, {\\theta}, {\\phi}\\right) & \\longmapsto & \\left(U, V, X, Y, Z\\right) = \\left({\\ell} \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right) \\cosh\\left({\\rho}\\right), {\\ell} \\cosh\\left({\\rho}\\right) \\sin\\left(\\frac{{\\tau}}{{\\ell}}\\right), {\\ell} \\cos\\left({\\phi}\\right) \\sin\\left({\\theta}\\right) \\sinh\\left({\\rho}\\right), {\\ell} \\sin\\left({\\phi}\\right) \\sin\\left({\\theta}\\right) \\sinh\\left({\\rho}\\right), {\\ell} \\cos\\left({\\theta}\\right) \\sinh\\left({\\rho}\\right)\\right) \\\\ \\mbox{on}\\ \\mathcal{M}_0 : & \\left({\\tau}, R, {\\theta}, {\\phi}\\right) & \\longmapsto & \\left(U, V, X, Y, Z\\right) = \\left(\\sqrt{R^{2} + {\\ell}^{2}} \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right), \\sqrt{R^{2} + {\\ell}^{2}} \\sin\\left(\\frac{{\\tau}}{{\\ell}}\\right), R \\cos\\left({\\phi}\\right) \\sin\\left({\\theta}\\right), R \\sin\\left({\\phi}\\right) \\sin\\left({\\theta}\\right), R \\cos\\left({\\theta}\\right)\\right) \\\\ \\mbox{on}\\ \\mathcal{M}_0 : & \\left({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi}\\right) & \\longmapsto & \\left(U, V, X, Y, Z\\right) = \\left(\\frac{{\\ell} \\cos\\left({\\tilde{\\tau}}\\right)}{\\cos\\left({\\chi}\\right)}, \\frac{{\\ell} \\sin\\left({\\tilde{\\tau}}\\right)}{\\cos\\left({\\chi}\\right)}, \\frac{{\\ell} \\cos\\left({\\phi}\\right) \\sin\\left({\\chi}\\right) \\sin\\left({\\theta}\\right)}{\\cos\\left({\\chi}\\right)}, \\frac{{\\ell} \\sin\\left({\\chi}\\right) \\sin\\left({\\phi}\\right) \\sin\\left({\\theta}\\right)}{\\cos\\left({\\chi}\\right)}, \\frac{{\\ell} \\cos\\left({\\theta}\\right) \\sin\\left({\\chi}\\right)}{\\cos\\left({\\chi}\\right)}\\right) \\end{array}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} \\Phi:& \\mathcal{M} & \\longrightarrow & \\mathbb{R}^{2,3} \\\\ \\mbox{on}\\ \\mathcal{M}_0 : & \\left({\\tau}, {\\rho}, {\\theta}, {\\phi}\\right) & \\longmapsto & \\left(U, V, X, Y, Z\\right) = \\left({\\ell} \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right) \\cosh\\left({\\rho}\\right), {\\ell} \\cosh\\left({\\rho}\\right) \\sin\\left(\\frac{{\\tau}}{{\\ell}}\\right), {\\ell} \\cos\\left({\\phi}\\right) \\sin\\left({\\theta}\\right) \\sinh\\left({\\rho}\\right), {\\ell} \\sin\\left({\\phi}\\right) \\sin\\left({\\theta}\\right) \\sinh\\left({\\rho}\\right), {\\ell} \\cos\\left({\\theta}\\right) \\sinh\\left({\\rho}\\right)\\right) \\\\ \\mbox{on}\\ \\mathcal{M}_0 : & \\left({\\tau}, R, {\\theta}, {\\phi}\\right) & \\longmapsto & \\left(U, V, X, Y, Z\\right) = \\left(\\sqrt{R^{2} + {\\ell}^{2}} \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right), \\sqrt{R^{2} + {\\ell}^{2}} \\sin\\left(\\frac{{\\tau}}{{\\ell}}\\right), R \\cos\\left({\\phi}\\right) \\sin\\left({\\theta}\\right), R \\sin\\left({\\phi}\\right) \\sin\\left({\\theta}\\right), R \\cos\\left({\\theta}\\right)\\right) \\\\ \\mbox{on}\\ \\mathcal{M}_0 : & \\left({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi}\\right) & \\longmapsto & \\left(U, V, X, Y, Z\\right) = \\left(\\frac{{\\ell} \\cos\\left({\\tilde{\\tau}}\\right)}{\\cos\\left({\\chi}\\right)}, \\frac{{\\ell} \\sin\\left({\\tilde{\\tau}}\\right)}{\\cos\\left({\\chi}\\right)}, \\frac{{\\ell} \\cos\\left({\\phi}\\right) \\sin\\left({\\chi}\\right) \\sin\\left({\\theta}\\right)}{\\cos\\left({\\chi}\\right)}, \\frac{{\\ell} \\sin\\left({\\chi}\\right) \\sin\\left({\\phi}\\right) \\sin\\left({\\theta}\\right)}{\\cos\\left({\\chi}\\right)}, \\frac{{\\ell} \\cos\\left({\\theta}\\right) \\sin\\left({\\chi}\\right)}{\\cos\\left({\\chi}\\right)}\\right) \\end{array}$$" ], "text/plain": [ "Phi: M → R23\n", "on M_0: (ta, rh, th, ph) ↦ (U, V, X, Y, Z) = (l*cos(ta/l)*cosh(rh), l*cosh(rh)*sin(ta/l), l*cos(ph)*sin(th)*sinh(rh), l*sin(ph)*sin(th)*sinh(rh), l*cos(th)*sinh(rh))\n", "on M_0: (ta, R, th, ph) ↦ (U, V, X, Y, Z) = (sqrt(R^2 + l^2)*cos(ta/l), sqrt(R^2 + l^2)*sin(ta/l), R*cos(ph)*sin(th), R*sin(ph)*sin(th), R*cos(th))\n", "on M_0: (tat, ch, th, ph) ↦ (U, V, X, Y, Z) = (l*cos(tat)/cos(ch), l*sin(tat)/cos(ch), l*cos(ph)*sin(ch)*sin(th)/cos(ch), l*sin(ch)*sin(ph)*sin(th)/cos(ch), l*cos(th)*sin(ch)/cos(ch))" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Phi.display()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we see that $U-X>0$ is equivalent to each of the following conditions:\n", "\n", "- in hyperboloidal coordinates:" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}-{\\ell} \\cos\\left({\\phi}\\right) \\sin\\left({\\theta}\\right) \\sinh\\left({\\rho}\\right) + {\\ell} \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right) \\cosh\\left({\\rho}\\right) > 0\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}-{\\ell} \\cos\\left({\\phi}\\right) \\sin\\left({\\theta}\\right) \\sinh\\left({\\rho}\\right) + {\\ell} \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right) \\cosh\\left({\\rho}\\right) > 0$$" ], "text/plain": [ "-l*cos(ph)*sin(th)*sinh(rh) + l*cos(ta/l)*cosh(rh) > 0" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Phi.expr(X_hyp, X23)[0] - Phi.expr(X_hyp, X23)[2] > 0 " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- in static coordinates:" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}-R \\cos\\left({\\phi}\\right) \\sin\\left({\\theta}\\right) + \\sqrt{R^{2} + {\\ell}^{2}} \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right) > 0\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}-R \\cos\\left({\\phi}\\right) \\sin\\left({\\theta}\\right) + \\sqrt{R^{2} + {\\ell}^{2}} \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right) > 0$$" ], "text/plain": [ "-R*cos(ph)*sin(th) + sqrt(R^2 + l^2)*cos(ta/l) > 0" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Phi.expr(X_stat, X23)[0] - Phi.expr(X_stat, X23)[2] > 0 " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- in conformal coordinates:" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}-\\frac{{\\ell} \\cos\\left({\\phi}\\right) \\sin\\left({\\chi}\\right) \\sin\\left({\\theta}\\right)}{\\cos\\left({\\chi}\\right)} + \\frac{{\\ell} \\cos\\left({\\tilde{\\tau}}\\right)}{\\cos\\left({\\chi}\\right)} > 0\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}-\\frac{{\\ell} \\cos\\left({\\phi}\\right) \\sin\\left({\\chi}\\right) \\sin\\left({\\theta}\\right)}{\\cos\\left({\\chi}\\right)} + \\frac{{\\ell} \\cos\\left({\\tilde{\\tau}}\\right)}{\\cos\\left({\\chi}\\right)} > 0$$" ], "text/plain": [ "-l*cos(ph)*sin(ch)*sin(th)/cos(ch) + l*cos(tat)/cos(ch) > 0" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Phi.expr(X_conf, X23)[0] - Phi.expr(X_conf, X23)[2] > 0 " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hence we declare:" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Open subset MP of the 4-dimensional Lorentzian manifold M\n" ] } ], "source": [ "MP = M0.open_subset('MP', latex_name=r'\\mathcal{M}_{\\rm P}', \n", " coord_def={X_hyp: cos(ta/l) - tanh(rh)*sin(th)*cos(ph)>0,\n", " X_stat: cos(ta/l) - R/sqrt(R^2+l^2)*sin(th)*cos(ph)>0,\n", " X_conf: cos(tat) - sin(ch)*sin(th)*cos(ph)>0})\n", "print(MP)" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Coordinate definition of the Poincaré patch in different charts:\n" ] }, { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left(\\mathcal{M}_{\\rm P},({\\tau}, {\\rho}, {\\theta}, {\\phi})\\right)\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left(\\mathcal{M}_{\\rm P},({\\tau}, {\\rho}, {\\theta}, {\\phi})\\right)$$" ], "text/plain": [ "Chart (MP, (ta, rh, th, ph))" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left[-\\cos\\left({\\phi}\\right) \\sin\\left({\\theta}\\right) \\tanh\\left({\\rho}\\right) + \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right) > 0\\right]\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left[-\\cos\\left({\\phi}\\right) \\sin\\left({\\theta}\\right) \\tanh\\left({\\rho}\\right) + \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right) > 0\\right]$$" ], "text/plain": [ "[-cos(ph)*sin(th)*tanh(rh) + cos(ta/l) > 0]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left(\\mathcal{M}_{\\rm P},({\\tau}, R, {\\theta}, {\\phi})\\right)\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left(\\mathcal{M}_{\\rm P},({\\tau}, R, {\\theta}, {\\phi})\\right)$$" ], "text/plain": [ "Chart (MP, (ta, R, th, ph))" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left[-\\frac{R \\cos\\left({\\phi}\\right) \\sin\\left({\\theta}\\right)}{\\sqrt{R^{2} + {\\ell}^{2}}} + \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right) > 0\\right]\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left[-\\frac{R \\cos\\left({\\phi}\\right) \\sin\\left({\\theta}\\right)}{\\sqrt{R^{2} + {\\ell}^{2}}} + \\cos\\left(\\frac{{\\tau}}{{\\ell}}\\right) > 0\\right]$$" ], "text/plain": [ "[-R*cos(ph)*sin(th)/sqrt(R^2 + l^2) + cos(ta/l) > 0]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left(\\mathcal{M}_{\\rm P},({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi})\\right)\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left(\\mathcal{M}_{\\rm P},({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi})\\right)$$" ], "text/plain": [ "Chart (MP, (tat, ch, th, ph))" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left[-\\cos\\left({\\phi}\\right) \\sin\\left({\\chi}\\right) \\sin\\left({\\theta}\\right) + \\cos\\left({\\tilde{\\tau}}\\right) > 0\\right]\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left[-\\cos\\left({\\phi}\\right) \\sin\\left({\\chi}\\right) \\sin\\left({\\theta}\\right) + \\cos\\left({\\tilde{\\tau}}\\right) > 0\\right]$$" ], "text/plain": [ "[-cos(ph)*sin(ch)*sin(th) + cos(tat) > 0]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(\"Coordinate definition of the Poincaré patch in different charts:\")\n", "for chart in MP.atlas():\n", " show(chart)\n", " show(chart._restrictions)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A view of the Poincaré patch on the AdS hyperboloid in $\\mathbb{R}^{2,3}$:" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "Graphics3d Object" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "graph = X_hyp.restrict(MP).plot(X23, mapping=Phi, ambient_coords=(V,X,U), \n", " fixed_coords={th: pi/2, ph:0}, \n", " ranges={ta:(0,2*pi), rh:(0,2)}, number_values={ta: 24, rh: 11}, \n", " color={ta:'red', rh:'grey'}, thickness=2, parameters={l:1}, \n", " label_axes=False)\n", "graph += X_hyp.restrict(MP).plot(X23, mapping=Phi, ambient_coords=(V,X,U), \n", " fixed_coords={th: pi/2, ph:pi},\n", " ranges={ta:(0,2*pi), rh:(0,2)}, number_values={ta: 24, rh: 11}, \n", " color={ta:'red', rh:'grey'}, thickness=2, parameters={l:1}, \n", " label_axes=False)\n", "graph += hyperboloid\n", "show(graph, aspect_ratio=1, axes_labels=['V','X','U'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A view of the Poincaré patch on the Einstein cylinder:" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "Graphics3d Object" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "graph = X_stat.restrict(MP).plot(X5, ambient_coords=(W,X,T), mapping=Theta,\n", " fixed_coords={th: pi/2, ph: 0}, \n", " ranges={ta: (-pi,pi), R: (0, 10)}, number_values={ta: 15, R: 20},\n", " parameters={l:1}, color={ta: 'red', R: 'grey'}, \n", " label_axes=False) # phi = 0 \n", "graph += X_stat.restrict(MP).plot(X5, ambient_coords=(W,X,T), mapping=Theta,\n", " fixed_coords={th: pi/2, ph: pi}, \n", " ranges={ta: (-pi,pi), R: (0, 10)}, number_values={ta: 15, R: 20},\n", " parameters={l:1}, color={ta: 'red', R: 'grey'}, \n", " label_axes=False) # phi = pi \n", "graph += graphE + half_cylinder\n", "show(graph, aspect_ratio=1, axes_labels=['W','X','tau'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us add the Poincaré horizon:" ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "Graphics3d Object" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "poincare_hor = parametric_plot3d([cos(ch), sin(ch), acos(-sin(ch))-pi], (ch, 0, pi/2),\n", " color='green', thickness=2) + \\\n", " parametric_plot3d([cos(ch), sin(ch), -acos(-sin(ch))+pi], (ch, 0, pi/2),\n", " color='green', thickness=2) + \\\n", " parametric_plot3d([cos(ch), -sin(ch), acos(sin(ch))-pi], (ch, 0, pi/2),\n", " color='green', thickness=2) + \\\n", " parametric_plot3d([cos(ch), -sin(ch), -acos(sin(ch))+pi], (ch, 0, pi/2),\n", " color='green', thickness=2)\n", "graph += poincare_hor\n", "show(graph, aspect_ratio=1, axes_labels=['W','X','tau'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Poincaré patch in the alternative cylindrical view of AdS spacetime based on the \n", "$(T,X,Y)$ coordinates of $\\mathbb{R}^5$:" ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "Graphics3d Object" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "graph = X_conf.restrict(MP).plot(X5, ambient_coords=(X,Y,T), mapping=Theta,\n", " fixed_coords={th: 0.9*pi/2}, \n", " ranges={tat: (-pi,pi), ch: (0,pi/2), ph:(0,2*pi)}, \n", " number_values=6, parameters={l:1}, \n", " color={tat: 'red', ch: 'grey', ph: 'orange'}, \n", " label_axes=False)\n", "show(graph+graphE, aspect_ratio=(1,1,0.5), axes_labels=['X','Y','tau'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We define the **Poincaré coordinates** $(t,x,y,u)$ on $\\mathcal{M}_{\\rm P}$ as" ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left(\\mathcal{M}_{\\rm P},(t, x, y, u)\\right)\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left(\\mathcal{M}_{\\rm P},(t, x, y, u)\\right)$$" ], "text/plain": [ "Chart (MP, (t, x, y, u))" ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_Poinc. = MP.chart('t x y u:(0,+oo)')\n", "X_Poinc" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}t :\\ \\left( -\\infty, +\\infty \\right) ;\\quad x :\\ \\left( -\\infty, +\\infty \\right) ;\\quad y :\\ \\left( -\\infty, +\\infty \\right) ;\\quad u :\\ \\left( 0 , +\\infty \\right)\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}t :\\ \\left( -\\infty, +\\infty \\right) ;\\quad x :\\ \\left( -\\infty, +\\infty \\right) ;\\quad y :\\ \\left( -\\infty, +\\infty \\right) ;\\quad u :\\ \\left( 0 , +\\infty \\right)$$" ], "text/plain": [ "t: (-oo, +oo); x: (-oo, +oo); y: (-oo, +oo); u: (0, +oo)" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_Poinc.coord_range()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The link between the conformal coordinates and the Poincaré ones is" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left\\{\\begin{array}{lcl} t & = & -\\frac{{\\ell} \\sin\\left({\\tilde{\\tau}}\\right)}{\\cos\\left({\\phi}\\right) \\sin\\left({\\chi}\\right) \\sin\\left({\\theta}\\right) - \\cos\\left({\\tilde{\\tau}}\\right)} \\\\ x & = & -\\frac{{\\ell} \\sin\\left({\\chi}\\right) \\sin\\left({\\phi}\\right) \\sin\\left({\\theta}\\right)}{\\cos\\left({\\phi}\\right) \\sin\\left({\\chi}\\right) \\sin\\left({\\theta}\\right) - \\cos\\left({\\tilde{\\tau}}\\right)} \\\\ y & = & -\\frac{{\\ell} \\cos\\left({\\theta}\\right) \\sin\\left({\\chi}\\right)}{\\cos\\left({\\phi}\\right) \\sin\\left({\\chi}\\right) \\sin\\left({\\theta}\\right) - \\cos\\left({\\tilde{\\tau}}\\right)} \\\\ u & = & -\\frac{{\\left(\\cos\\left({\\phi}\\right) \\sin\\left({\\chi}\\right) \\sin\\left({\\theta}\\right) - \\cos\\left({\\tilde{\\tau}}\\right)\\right)} {\\ell}}{\\cos\\left({\\chi}\\right)} \\end{array}\\right.\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left\\{\\begin{array}{lcl} t & = & -\\frac{{\\ell} \\sin\\left({\\tilde{\\tau}}\\right)}{\\cos\\left({\\phi}\\right) \\sin\\left({\\chi}\\right) \\sin\\left({\\theta}\\right) - \\cos\\left({\\tilde{\\tau}}\\right)} \\\\ x & = & -\\frac{{\\ell} \\sin\\left({\\chi}\\right) \\sin\\left({\\phi}\\right) \\sin\\left({\\theta}\\right)}{\\cos\\left({\\phi}\\right) \\sin\\left({\\chi}\\right) \\sin\\left({\\theta}\\right) - \\cos\\left({\\tilde{\\tau}}\\right)} \\\\ y & = & -\\frac{{\\ell} \\cos\\left({\\theta}\\right) \\sin\\left({\\chi}\\right)}{\\cos\\left({\\phi}\\right) \\sin\\left({\\chi}\\right) \\sin\\left({\\theta}\\right) - \\cos\\left({\\tilde{\\tau}}\\right)} \\\\ u & = & -\\frac{{\\left(\\cos\\left({\\phi}\\right) \\sin\\left({\\chi}\\right) \\sin\\left({\\theta}\\right) - \\cos\\left({\\tilde{\\tau}}\\right)\\right)} {\\ell}}{\\cos\\left({\\chi}\\right)} \\end{array}\\right.$$" ], "text/plain": [ "t = -l*sin(tat)/(cos(ph)*sin(ch)*sin(th) - cos(tat))\n", "x = -l*sin(ch)*sin(ph)*sin(th)/(cos(ph)*sin(ch)*sin(th) - cos(tat))\n", "y = -l*cos(th)*sin(ch)/(cos(ph)*sin(ch)*sin(th) - cos(tat))\n", "u = -(cos(ph)*sin(ch)*sin(th) - cos(tat))*l/cos(ch)" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "conf_to_Poinc = X_conf.restrict(MP).transition_map(X_Poinc, \n", " [l*sin(tat)/(cos(tat) - sin(ch)*sin(th)*cos(ph)),\n", " l*sin(ch)*sin(th)*sin(ph)/(cos(tat) - sin(ch)*sin(th)*cos(ph)),\n", " l*sin(ch)*cos(th)/(cos(tat) - sin(ch)*sin(th)*cos(ph)),\n", " l*(cos(tat) - sin(ch)*sin(th)*cos(ph))/cos(ch)])\n", "conf_to_Poinc.display()" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Check of the inverse coordinate transformation:\n", " tat == -arctan2(2*l^2*sin(tat)/(cos(ph)*sin(ch)*sin(th) - cos(tat)), -2*l^2*cos(tat)/(cos(ph)*sin(ch)*sin(th) - cos(tat))) **failed**\n", " ch == pi - arccos(abs(cos(ph)*sin(ch)*sin(th) - cos(tat))*cos(ch)/(cos(ph)*sin(ch)*sin(th) - cos(tat))) **failed**\n", " th == pi - arccos(abs(cos(ph)*sin(ch)*sin(th) - cos(tat))*cos(th)/(cos(ph)*sin(ch)*sin(th) - cos(tat))) **failed**\n", " ph == -arctan2(2*l^2*sin(ch)*sin(ph)*sin(th)/(cos(ph)*sin(ch)*sin(th) - cos(tat)), -2*l^2*cos(ph)*sin(ch)*sin(th)/(cos(ph)*sin(ch)*sin(th) - cos(tat))) **failed**\n", " t == t *passed*\n", " x == x *passed*\n", " y == y *passed*\n", " u == u *passed*\n", "NB: a failed report can reflect a mere lack of simplification.\n" ] } ], "source": [ "conf_to_Poinc.set_inverse(atan2(2*l*t, x^2+y^2-t^2+l^2*(1+l^2/u^2)),\n", " acos(2*l^3/u/sqrt((x^2+y^2-t^2+l^2*(1+l^2/u^2))^2 + 4*l^2*t^2)),\n", " acos(2*l*y/sqrt((x^2+y^2-t^2+l^2*(1+l^2/u^2))^2\n", " + 4*l^2*(t^2-l^4/u^2))),\n", " atan2(2*l*x, x^2+y^2-t^2-l^2*(1-l^2/u^2))) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The test is passed, modulo some lack of simplification in the `arctan2` and `arccos` functions." ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left\\{\\begin{array}{lcl} {\\tilde{\\tau}} & = & \\arctan\\left(2 \\, {\\ell} t, {\\ell}^{2} {\\left(\\frac{{\\ell}^{2}}{u^{2}} + 1\\right)} - t^{2} + x^{2} + y^{2}\\right) \\\\ {\\chi} & = & \\arccos\\left(\\frac{2 \\, {\\ell}^{3}}{\\sqrt{4 \\, {\\ell}^{2} t^{2} + {\\left({\\ell}^{2} {\\left(\\frac{{\\ell}^{2}}{u^{2}} + 1\\right)} - t^{2} + x^{2} + y^{2}\\right)}^{2}} u}\\right) \\\\ {\\theta} & = & \\arccos\\left(\\frac{2 \\, {\\ell} y}{\\sqrt{4 \\, {\\left(t^{2} - \\frac{{\\ell}^{4}}{u^{2}}\\right)} {\\ell}^{2} + {\\left({\\ell}^{2} {\\left(\\frac{{\\ell}^{2}}{u^{2}} + 1\\right)} - t^{2} + x^{2} + y^{2}\\right)}^{2}}}\\right) \\\\ {\\phi} & = & \\arctan\\left(2 \\, {\\ell} x, {\\ell}^{2} {\\left(\\frac{{\\ell}^{2}}{u^{2}} - 1\\right)} - t^{2} + x^{2} + y^{2}\\right) \\end{array}\\right.\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left\\{\\begin{array}{lcl} {\\tilde{\\tau}} & = & \\arctan\\left(2 \\, {\\ell} t, {\\ell}^{2} {\\left(\\frac{{\\ell}^{2}}{u^{2}} + 1\\right)} - t^{2} + x^{2} + y^{2}\\right) \\\\ {\\chi} & = & \\arccos\\left(\\frac{2 \\, {\\ell}^{3}}{\\sqrt{4 \\, {\\ell}^{2} t^{2} + {\\left({\\ell}^{2} {\\left(\\frac{{\\ell}^{2}}{u^{2}} + 1\\right)} - t^{2} + x^{2} + y^{2}\\right)}^{2}} u}\\right) \\\\ {\\theta} & = & \\arccos\\left(\\frac{2 \\, {\\ell} y}{\\sqrt{4 \\, {\\left(t^{2} - \\frac{{\\ell}^{4}}{u^{2}}\\right)} {\\ell}^{2} + {\\left({\\ell}^{2} {\\left(\\frac{{\\ell}^{2}}{u^{2}} + 1\\right)} - t^{2} + x^{2} + y^{2}\\right)}^{2}}}\\right) \\\\ {\\phi} & = & \\arctan\\left(2 \\, {\\ell} x, {\\ell}^{2} {\\left(\\frac{{\\ell}^{2}}{u^{2}} - 1\\right)} - t^{2} + x^{2} + y^{2}\\right) \\end{array}\\right.$$" ], "text/plain": [ "tat = arctan2(2*l*t, l^2*(l^2/u^2 + 1) - t^2 + x^2 + y^2)\n", "ch = arccos(2*l^3/(sqrt(4*l^2*t^2 + (l^2*(l^2/u^2 + 1) - t^2 + x^2 + y^2)^2)*u))\n", "th = arccos(2*l*y/sqrt(4*(t^2 - l^4/u^2)*l^2 + (l^2*(l^2/u^2 + 1) - t^2 + x^2 + y^2)^2))\n", "ph = arctan2(2*l*x, l^2*(l^2/u^2 - 1) - t^2 + x^2 + y^2)" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "conf_to_Poinc.inverse().display()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The isometric immersion $\\Phi$ expressed in terms of conformal coordinates on the Poincaré patch:" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} \\Phi:& \\mathcal{M}_{\\rm P} & \\longrightarrow & \\mathbb{R}^{2,3} \\\\ & \\left({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi}\\right) & \\longmapsto & \\left(U, V, X, Y, Z\\right) = \\left(\\frac{{\\ell} \\cos\\left({\\tilde{\\tau}}\\right)}{\\cos\\left({\\chi}\\right)}, \\frac{{\\ell} \\sin\\left({\\tilde{\\tau}}\\right)}{\\cos\\left({\\chi}\\right)}, \\frac{{\\ell} \\cos\\left({\\phi}\\right) \\sin\\left({\\chi}\\right) \\sin\\left({\\theta}\\right)}{\\cos\\left({\\chi}\\right)}, \\frac{{\\ell} \\sin\\left({\\chi}\\right) \\sin\\left({\\phi}\\right) \\sin\\left({\\theta}\\right)}{\\cos\\left({\\chi}\\right)}, \\frac{{\\ell} \\cos\\left({\\theta}\\right) \\sin\\left({\\chi}\\right)}{\\cos\\left({\\chi}\\right)}\\right) \\end{array}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} \\Phi:& \\mathcal{M}_{\\rm P} & \\longrightarrow & \\mathbb{R}^{2,3} \\\\ & \\left({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi}\\right) & \\longmapsto & \\left(U, V, X, Y, Z\\right) = \\left(\\frac{{\\ell} \\cos\\left({\\tilde{\\tau}}\\right)}{\\cos\\left({\\chi}\\right)}, \\frac{{\\ell} \\sin\\left({\\tilde{\\tau}}\\right)}{\\cos\\left({\\chi}\\right)}, \\frac{{\\ell} \\cos\\left({\\phi}\\right) \\sin\\left({\\chi}\\right) \\sin\\left({\\theta}\\right)}{\\cos\\left({\\chi}\\right)}, \\frac{{\\ell} \\sin\\left({\\chi}\\right) \\sin\\left({\\phi}\\right) \\sin\\left({\\theta}\\right)}{\\cos\\left({\\chi}\\right)}, \\frac{{\\ell} \\cos\\left({\\theta}\\right) \\sin\\left({\\chi}\\right)}{\\cos\\left({\\chi}\\right)}\\right) \\end{array}$$" ], "text/plain": [ "Phi: MP → R23\n", " (tat, ch, th, ph) ↦ (U, V, X, Y, Z) = (l*cos(tat)/cos(ch), l*sin(tat)/cos(ch), l*cos(ph)*sin(ch)*sin(th)/cos(ch), l*sin(ch)*sin(ph)*sin(th)/cos(ch), l*cos(th)*sin(ch)/cos(ch))" ] }, "execution_count": 95, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Phi.restrict(MP).display(X_conf.restrict(MP), X23)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The isometric immersion $\\Phi$ expressed in terms of Poincaré coordinates:" ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} \\Phi:& \\mathcal{M}_{\\rm P} & \\longrightarrow & \\mathbb{R}^{2,3} \\\\ & \\left(t, x, y, u\\right) & \\longmapsto & \\left(U, V, X, Y, Z\\right) = \\left(\\frac{{\\ell}^{4} + u^{2} x^{2} + u^{2} y^{2} + {\\left({\\ell}^{2} - t^{2}\\right)} u^{2}}{2 \\, {\\ell}^{2} u}, \\frac{t u}{{\\ell}}, \\frac{{\\ell}^{4} + u^{2} x^{2} + u^{2} y^{2} - {\\left({\\ell}^{2} + t^{2}\\right)} u^{2}}{2 \\, {\\ell}^{2} u}, \\frac{u x}{{\\ell}}, \\frac{u y}{{\\ell}}\\right) \\end{array}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} \\Phi:& \\mathcal{M}_{\\rm P} & \\longrightarrow & \\mathbb{R}^{2,3} \\\\ & \\left(t, x, y, u\\right) & \\longmapsto & \\left(U, V, X, Y, Z\\right) = \\left(\\frac{{\\ell}^{4} + u^{2} x^{2} + u^{2} y^{2} + {\\left({\\ell}^{2} - t^{2}\\right)} u^{2}}{2 \\, {\\ell}^{2} u}, \\frac{t u}{{\\ell}}, \\frac{{\\ell}^{4} + u^{2} x^{2} + u^{2} y^{2} - {\\left({\\ell}^{2} + t^{2}\\right)} u^{2}}{2 \\, {\\ell}^{2} u}, \\frac{u x}{{\\ell}}, \\frac{u y}{{\\ell}}\\right) \\end{array}$$" ], "text/plain": [ "Phi: MP → R23\n", " (t, x, y, u) ↦ (U, V, X, Y, Z) = (1/2*(l^4 + u^2*x^2 + u^2*y^2 + (l^2 - t^2)*u^2)/(l^2*u), t*u/l, 1/2*(l^4 + u^2*x^2 + u^2*y^2 - (l^2 + t^2)*u^2)/(l^2*u), u*x/l, u*y/l)" ] }, "execution_count": 96, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Phi.restrict(MP).display(X_Poinc, X23)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that the coordinate $u$ is simply $U-X$:" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}u\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}u$$" ], "text/plain": [ "u" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "U1 = Phi.restrict(MP).expr(X_Poinc, X23)[0]\n", "X1 = Phi.restrict(MP).expr(X_Poinc, X23)[2]\n", "(U1 - X1).simplify_full()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "so that the Poincaré horizon corresponds to $u\\rightarrow 0$. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Metric in Poincaré coordinates\n", "\n", "Let us ask Sage to compute the metric components in terms of the Poincaré coordinates:" ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}g = -\\frac{u^{2}}{{\\ell}^{2}} \\mathrm{d} t\\otimes \\mathrm{d} t + \\frac{u^{2}}{{\\ell}^{2}} \\mathrm{d} x\\otimes \\mathrm{d} x + \\frac{u^{2}}{{\\ell}^{2}} \\mathrm{d} y\\otimes \\mathrm{d} y + \\frac{{\\ell}^{2}}{u^{2}} \\mathrm{d} u\\otimes \\mathrm{d} u\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}g = -\\frac{u^{2}}{{\\ell}^{2}} \\mathrm{d} t\\otimes \\mathrm{d} t + \\frac{u^{2}}{{\\ell}^{2}} \\mathrm{d} x\\otimes \\mathrm{d} x + \\frac{u^{2}}{{\\ell}^{2}} \\mathrm{d} y\\otimes \\mathrm{d} y + \\frac{{\\ell}^{2}}{u^{2}} \\mathrm{d} u\\otimes \\mathrm{d} u$$" ], "text/plain": [ "g = -u^2/l^2 dt⊗dt + u^2/l^2 dx⊗dx + u^2/l^2 dy⊗dy + l^2/u^2 du⊗du" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g.display(X_Poinc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We notice that the metric restricted to the hypersurfaces $u=\\mathrm{const}$ (i.e. the intersection of the hyperboloid with hyperplanes $U-X = \\mathrm{const}$) is nothing but the Minkowski metric (up to some constant factor $u^2/\\ell^2$):\n", "$$ \\left. g \\right| _{u=\\mathrm{const}}= \\frac{u^2}{\\ell^2} \\left( \n", " -\\mathrm{d}t\\otimes\\mathrm{d}t + \n", " \\mathrm{d}x\\otimes\\mathrm{d}x + \\mathrm{d}y\\otimes\\mathrm{d}y \\right).$$\n", "$(t,x,y)$ appear then as Minkowskian coordinates of these hypersurfaces. We may say that $\\mathcal{M}_{\\rm P}$ is sliced by a family, parametrized by $u$, of flat 3-dimensional spacetimes. " ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left\\{\\begin{array}{lcl} {\\tau} & = & {\\ell} \\arctan\\left(2 \\, {\\ell} t, \\frac{{\\ell}^{4} + u^{2} x^{2} + u^{2} y^{2} + {\\left({\\ell}^{2} - t^{2}\\right)} u^{2}}{u^{2}}\\right) \\\\ R & = & \\frac{\\sqrt{{\\ell}^{8} + u^{4} x^{4} + u^{4} y^{4} + {\\left({\\ell}^{4} + 2 \\, {\\ell}^{2} t^{2} + t^{4}\\right)} u^{4} - 2 \\, {\\left({\\ell}^{6} + {\\ell}^{4} t^{2}\\right)} u^{2} + 2 \\, {\\left({\\ell}^{4} u^{2} + {\\left({\\ell}^{2} - t^{2}\\right)} u^{4}\\right)} x^{2} + 2 \\, {\\left({\\ell}^{4} u^{2} + u^{4} x^{2} + {\\left({\\ell}^{2} - t^{2}\\right)} u^{4}\\right)} y^{2}}}{2 \\, {\\ell}^{2} u} \\\\ {\\theta} & = & \\arccos\\left(\\frac{2 \\, {\\ell} u^{2} y}{\\sqrt{{\\ell}^{8} + u^{4} x^{4} + u^{4} y^{4} + {\\left({\\ell}^{4} + 2 \\, {\\ell}^{2} t^{2} + t^{4}\\right)} u^{4} - 2 \\, {\\left({\\ell}^{6} + {\\ell}^{4} t^{2}\\right)} u^{2} + 2 \\, {\\left({\\ell}^{4} u^{2} + {\\left({\\ell}^{2} - t^{2}\\right)} u^{4}\\right)} x^{2} + 2 \\, {\\left({\\ell}^{4} u^{2} + u^{4} x^{2} + {\\left({\\ell}^{2} - t^{2}\\right)} u^{4}\\right)} y^{2}}}\\right) \\\\ {\\phi} & = & \\arctan\\left(2 \\, {\\ell} x, \\frac{{\\ell}^{4} + u^{2} x^{2} + u^{2} y^{2} - {\\left({\\ell}^{2} + t^{2}\\right)} u^{2}}{u^{2}}\\right) \\end{array}\\right.\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left\\{\\begin{array}{lcl} {\\tau} & = & {\\ell} \\arctan\\left(2 \\, {\\ell} t, \\frac{{\\ell}^{4} + u^{2} x^{2} + u^{2} y^{2} + {\\left({\\ell}^{2} - t^{2}\\right)} u^{2}}{u^{2}}\\right) \\\\ R & = & \\frac{\\sqrt{{\\ell}^{8} + u^{4} x^{4} + u^{4} y^{4} + {\\left({\\ell}^{4} + 2 \\, {\\ell}^{2} t^{2} + t^{4}\\right)} u^{4} - 2 \\, {\\left({\\ell}^{6} + {\\ell}^{4} t^{2}\\right)} u^{2} + 2 \\, {\\left({\\ell}^{4} u^{2} + {\\left({\\ell}^{2} - t^{2}\\right)} u^{4}\\right)} x^{2} + 2 \\, {\\left({\\ell}^{4} u^{2} + u^{4} x^{2} + {\\left({\\ell}^{2} - t^{2}\\right)} u^{4}\\right)} y^{2}}}{2 \\, {\\ell}^{2} u} \\\\ {\\theta} & = & \\arccos\\left(\\frac{2 \\, {\\ell} u^{2} y}{\\sqrt{{\\ell}^{8} + u^{4} x^{4} + u^{4} y^{4} + {\\left({\\ell}^{4} + 2 \\, {\\ell}^{2} t^{2} + t^{4}\\right)} u^{4} - 2 \\, {\\left({\\ell}^{6} + {\\ell}^{4} t^{2}\\right)} u^{2} + 2 \\, {\\left({\\ell}^{4} u^{2} + {\\left({\\ell}^{2} - t^{2}\\right)} u^{4}\\right)} x^{2} + 2 \\, {\\left({\\ell}^{4} u^{2} + u^{4} x^{2} + {\\left({\\ell}^{2} - t^{2}\\right)} u^{4}\\right)} y^{2}}}\\right) \\\\ {\\phi} & = & \\arctan\\left(2 \\, {\\ell} x, \\frac{{\\ell}^{4} + u^{2} x^{2} + u^{2} y^{2} - {\\left({\\ell}^{2} + t^{2}\\right)} u^{2}}{u^{2}}\\right) \\end{array}\\right.$$" ], "text/plain": [ "ta = l*arctan2(2*l*t, (l^4 + u^2*x^2 + u^2*y^2 + (l^2 - t^2)*u^2)/u^2)\n", "R = 1/2*sqrt(l^8 + u^4*x^4 + u^4*y^4 + (l^4 + 2*l^2*t^2 + t^4)*u^4 - 2*(l^6 + l^4*t^2)*u^2 + 2*(l^4*u^2 + (l^2 - t^2)*u^4)*x^2 + 2*(l^4*u^2 + u^4*x^2 + (l^2 - t^2)*u^4)*y^2)/(l^2*u)\n", "th = arccos(2*l*u^2*y/sqrt(l^8 + u^4*x^4 + u^4*y^4 + (l^4 + 2*l^2*t^2 + t^4)*u^4 - 2*(l^6 + l^4*t^2)*u^2 + 2*(l^4*u^2 + (l^2 - t^2)*u^4)*x^2 + 2*(l^4*u^2 + u^4*x^2 + (l^2 - t^2)*u^4)*y^2))\n", "ph = arctan2(2*l*x, (l^4 + u^2*x^2 + u^2*y^2 - (l^2 + t^2)*u^2)/u^2)" ] }, "execution_count": 99, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Poinc_to_stat = stat_to_conf.inverse().restrict(MP) * conf_to_Poinc.inverse()\n", "Poinc_to_stat.display()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot of the Poincaré coordinates $(t,x)$ in terms of the static coordinates $(\\tau, R)$ for $y=0$ and $u=1$:" ] }, { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "Graphics object consisting of 18 graphics primitives" ] }, "execution_count": 100, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_Poinc.plot(X_stat.restrict(MP), ambient_coords=(R, ta), \n", " fixed_coords={y:0, u:1}, number_values=9, \n", " parameters={l:1}, plot_points=200,\n", " color={t: 'red', x: 'gold'})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot of the Poincaré coordinates $(t,u)$ in terms of $(U,V,X)$ for $(x,y)=(0,0)$:" ] }, { "cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "Graphics3d Object" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "graph = X_Poinc.plot(X23, mapping=Phi.restrict(MP), ambient_coords=(V,X,U), \n", " fixed_coords={x:0, y:0}, ranges={t:(-1,1), u:(0.1, 6)},\n", " number_values=13, color={t:'red', u: 'cyan'}, \n", " thickness=1, parameters={l:1}, label_axes=False)\n", "graph += hyperboloid\n", "show(graph, aspect_ratio=1, axes_labels=['V','X','U'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot of the Poincaré coordinates $(t,x,u)$ in terms of $(T,X,Y)$ for $y=0$:\n", "- the red lines are those along which $t$ varies at fixed $(x,u)$\n", "- the gold lines are those along which $x$ varies at fixed $(t,u)$\n", "- the cyan lines are those along which $u$ varies at fixed $(t,x)$" ] }, { "cell_type": "code", "execution_count": 102, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "Graphics3d Object" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "graph = X_Poinc.plot(X5, ambient_coords=(X,Y,T), mapping=Theta.restrict(MP),\n", " fixed_coords={y:0}, ranges={t:(-3,3), x:(-3,3), u:(0.01, 6)}, \n", " number_values=7, color={t:'red', x:'gold', u: 'cyan'}, \n", " parameters={l:1}, label_axes=False)\n", "graph += graphE\n", "show(graph, aspect_ratio=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Variant of Poincaré coordinates\n", "\n", "Instead of $u$, we may use the coordinate\n", "$$ z = \\frac{\\ell^2}{u}$$" ] }, { "cell_type": "code", "execution_count": 103, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left(\\mathcal{M}_{\\rm P},(t, x, y, z)\\right)\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left(\\mathcal{M}_{\\rm P},(t, x, y, z)\\right)$$" ], "text/plain": [ "Chart (MP, (t, x, y, z))" ] }, "execution_count": 103, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_Poinc_z. = MP.chart('t x y z:(0,+oo)')\n", "X_Poinc_z" ] }, { "cell_type": "code", "execution_count": 104, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}t :\\ \\left( -\\infty, +\\infty \\right) ;\\quad x :\\ \\left( -\\infty, +\\infty \\right) ;\\quad y :\\ \\left( -\\infty, +\\infty \\right) ;\\quad z :\\ \\left( 0 , +\\infty \\right)\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}t :\\ \\left( -\\infty, +\\infty \\right) ;\\quad x :\\ \\left( -\\infty, +\\infty \\right) ;\\quad y :\\ \\left( -\\infty, +\\infty \\right) ;\\quad z :\\ \\left( 0 , +\\infty \\right)$$" ], "text/plain": [ "t: (-oo, +oo); x: (-oo, +oo); y: (-oo, +oo); z: (0, +oo)" ] }, "execution_count": 104, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_Poinc_z.coord_range()" ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left\\{\\begin{array}{lcl} t & = & t \\\\ x & = & x \\\\ y & = & y \\\\ z & = & \\frac{{\\ell}^{2}}{u} \\end{array}\\right.\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left\\{\\begin{array}{lcl} t & = & t \\\\ x & = & x \\\\ y & = & y \\\\ z & = & \\frac{{\\ell}^{2}}{u} \\end{array}\\right.$$" ], "text/plain": [ "t = t\n", "x = x\n", "y = y\n", "z = l^2/u" ] }, "execution_count": 105, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Poinc_to_Poinc_z = X_Poinc.transition_map(X_Poinc_z, [t, x, y, l^2/u])\n", "Poinc_to_Poinc_z.display()" ] }, { "cell_type": "code", "execution_count": 106, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left\\{\\begin{array}{lcl} t & = & t \\\\ x & = & x \\\\ y & = & y \\\\ u & = & \\frac{{\\ell}^{2}}{z} \\end{array}\\right.\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left\\{\\begin{array}{lcl} t & = & t \\\\ x & = & x \\\\ y & = & y \\\\ u & = & \\frac{{\\ell}^{2}}{z} \\end{array}\\right.$$" ], "text/plain": [ "t = t\n", "x = x\n", "y = y\n", "u = l^2/z" ] }, "execution_count": 106, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Poinc_to_Poinc_z.inverse().display()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In terms of the coordinates $(t,x,y,z)$, the AdS metric looks like the metric of the 4-dimensional hyperbolic space in [\"half-plane\" Poincaré coordinates](http://nbviewer.jupyter.org/github/sagemanifolds/SageManifolds/blob/master/Worksheets/v1.3/SM_hyperbolic_plane.ipynb), except for the change of signature from $(+,+,+,+)$ to $(-,+,+,+)$:" ] }, { "cell_type": "code", "execution_count": 107, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}g = -\\frac{{\\ell}^{2}}{z^{2}} \\mathrm{d} t\\otimes \\mathrm{d} t + \\frac{{\\ell}^{2}}{z^{2}} \\mathrm{d} x\\otimes \\mathrm{d} x + \\frac{{\\ell}^{2}}{z^{2}} \\mathrm{d} y\\otimes \\mathrm{d} y + \\frac{{\\ell}^{2}}{z^{2}} \\mathrm{d} z\\otimes \\mathrm{d} z\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}g = -\\frac{{\\ell}^{2}}{z^{2}} \\mathrm{d} t\\otimes \\mathrm{d} t + \\frac{{\\ell}^{2}}{z^{2}} \\mathrm{d} x\\otimes \\mathrm{d} x + \\frac{{\\ell}^{2}}{z^{2}} \\mathrm{d} y\\otimes \\mathrm{d} y + \\frac{{\\ell}^{2}}{z^{2}} \\mathrm{d} z\\otimes \\mathrm{d} z$$" ], "text/plain": [ "g = -l^2/z^2 dt⊗dt + l^2/z^2 dx⊗dx + l^2/z^2 dy⊗dy + l^2/z^2 dz⊗dz" ] }, "execution_count": 107, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g.display(X_Poinc_z)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This justifies the name *Poincaré coordinates* given to $(t,x,y,u)$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The isometric immersion in $\\mathbb{R}^{2,3}$ in terms of the Poincaré coordinates $(t,x,y,z)$:" ] }, { "cell_type": "code", "execution_count": 108, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} \\Phi:& \\mathcal{M}_{\\rm P} & \\longrightarrow & \\mathbb{R}^{2,3} \\\\ & \\left(t, x, y, z\\right) & \\longmapsto & \\left(U, V, X, Y, Z\\right) = \\left(\\frac{{\\ell}^{2} - t^{2} + x^{2} + y^{2} + z^{2}}{2 \\, z}, \\frac{{\\ell} t}{z}, -\\frac{{\\ell}^{2} + t^{2} - x^{2} - y^{2} - z^{2}}{2 \\, z}, \\frac{{\\ell} x}{z}, \\frac{{\\ell} y}{z}\\right) \\end{array}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} \\Phi:& \\mathcal{M}_{\\rm P} & \\longrightarrow & \\mathbb{R}^{2,3} \\\\ & \\left(t, x, y, z\\right) & \\longmapsto & \\left(U, V, X, Y, Z\\right) = \\left(\\frac{{\\ell}^{2} - t^{2} + x^{2} + y^{2} + z^{2}}{2 \\, z}, \\frac{{\\ell} t}{z}, -\\frac{{\\ell}^{2} + t^{2} - x^{2} - y^{2} - z^{2}}{2 \\, z}, \\frac{{\\ell} x}{z}, \\frac{{\\ell} y}{z}\\right) \\end{array}$$" ], "text/plain": [ "Phi: MP → R23\n", " (t, x, y, z) ↦ (U, V, X, Y, Z) = (1/2*(l^2 - t^2 + x^2 + y^2 + z^2)/z, l*t/z, -1/2*(l^2 + t^2 - x^2 - y^2 - z^2)/z, l*x/z, l*y/z)" ] }, "execution_count": 108, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Phi.restrict(MP).display(X_Poinc_z, X23)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exponential Poincaré coordinates\n", "\n", "Another variant of Poincaré coordinates is by using $r$ instead of $u$, such that\n", "$$ u = \\ell e^{r/\\ell}$$" ] }, { "cell_type": "code", "execution_count": 109, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left(\\mathcal{M}_{\\rm P},(t, x, y, r)\\right)\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left(\\mathcal{M}_{\\rm P},(t, x, y, r)\\right)$$" ], "text/plain": [ "Chart (MP, (t, x, y, r))" ] }, "execution_count": 109, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_Poinc_exp. = MP.chart()\n", "X_Poinc_exp" ] }, { "cell_type": "code", "execution_count": 110, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}t :\\ \\left( -\\infty, +\\infty \\right) ;\\quad x :\\ \\left( -\\infty, +\\infty \\right) ;\\quad y :\\ \\left( -\\infty, +\\infty \\right) ;\\quad r :\\ \\left( -\\infty, +\\infty \\right)\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}t :\\ \\left( -\\infty, +\\infty \\right) ;\\quad x :\\ \\left( -\\infty, +\\infty \\right) ;\\quad y :\\ \\left( -\\infty, +\\infty \\right) ;\\quad r :\\ \\left( -\\infty, +\\infty \\right)$$" ], "text/plain": [ "t: (-oo, +oo); x: (-oo, +oo); y: (-oo, +oo); r: (-oo, +oo)" ] }, "execution_count": 110, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_Poinc_exp.coord_range()" ] }, { "cell_type": "code", "execution_count": 111, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left\\{\\begin{array}{lcl} t & = & t \\\\ x & = & x \\\\ y & = & y \\\\ r & = & {\\ell} \\log\\left(\\frac{u}{{\\ell}}\\right) \\end{array}\\right.\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left\\{\\begin{array}{lcl} t & = & t \\\\ x & = & x \\\\ y & = & y \\\\ r & = & {\\ell} \\log\\left(\\frac{u}{{\\ell}}\\right) \\end{array}\\right.$$" ], "text/plain": [ "t = t\n", "x = x\n", "y = y\n", "r = l*log(u/l)" ] }, "execution_count": 111, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Poinc_to_Poinc_exp = X_Poinc.transition_map(X_Poinc_exp, [t, x, y, l*ln(u/l)])\n", "Poinc_to_Poinc_exp.display()" ] }, { "cell_type": "code", "execution_count": 112, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left\\{\\begin{array}{lcl} t & = & t \\\\ x & = & x \\\\ y & = & y \\\\ u & = & {\\ell} e^{\\frac{r}{{\\ell}}} \\end{array}\\right.\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left\\{\\begin{array}{lcl} t & = & t \\\\ x & = & x \\\\ y & = & y \\\\ u & = & {\\ell} e^{\\frac{r}{{\\ell}}} \\end{array}\\right.$$" ], "text/plain": [ "t = t\n", "x = x\n", "y = y\n", "u = l*e^(r/l)" ] }, "execution_count": 112, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Poinc_to_Poinc_exp.inverse().display()" ] }, { "cell_type": "code", "execution_count": 113, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}g = -e^{\\left(\\frac{2 \\, r}{{\\ell}}\\right)} \\mathrm{d} t\\otimes \\mathrm{d} t + e^{\\left(\\frac{2 \\, r}{{\\ell}}\\right)} \\mathrm{d} x\\otimes \\mathrm{d} x + e^{\\left(\\frac{2 \\, r}{{\\ell}}\\right)} \\mathrm{d} y\\otimes \\mathrm{d} y +\\mathrm{d} r\\otimes \\mathrm{d} r\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}g = -e^{\\left(\\frac{2 \\, r}{{\\ell}}\\right)} \\mathrm{d} t\\otimes \\mathrm{d} t + e^{\\left(\\frac{2 \\, r}{{\\ell}}\\right)} \\mathrm{d} x\\otimes \\mathrm{d} x + e^{\\left(\\frac{2 \\, r}{{\\ell}}\\right)} \\mathrm{d} y\\otimes \\mathrm{d} y +\\mathrm{d} r\\otimes \\mathrm{d} r$$" ], "text/plain": [ "g = -e^(2*r/l) dt⊗dt + e^(2*r/l) dx⊗dx + e^(2*r/l) dy⊗dy + dr⊗dr" ] }, "execution_count": 113, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g.display(X_Poinc_exp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The isometric immersion in $\\mathbb{R}^{2,3}$ in terms of the coordinates $(t,x,y,r)$:" ] }, { "cell_type": "code", "execution_count": 114, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} \\Phi:& \\mathcal{M}_{\\rm P} & \\longrightarrow & \\mathbb{R}^{2,3} \\\\ & \\left(t, x, y, r\\right) & \\longmapsto & \\left(U, V, X, Y, Z\\right) = \\left(\\frac{{\\left({\\ell}^{2} e^{\\left(\\frac{2 \\, r}{{\\ell}}\\right)} - t^{2} e^{\\left(\\frac{2 \\, r}{{\\ell}}\\right)} + x^{2} e^{\\left(\\frac{2 \\, r}{{\\ell}}\\right)} + y^{2} e^{\\left(\\frac{2 \\, r}{{\\ell}}\\right)} + {\\ell}^{2}\\right)} e^{\\left(-\\frac{r}{{\\ell}}\\right)}}{2 \\, {\\ell}}, t e^{\\frac{r}{{\\ell}}}, -\\frac{{\\left({\\ell}^{2} e^{\\left(\\frac{2 \\, r}{{\\ell}}\\right)} + t^{2} e^{\\left(\\frac{2 \\, r}{{\\ell}}\\right)} - x^{2} e^{\\left(\\frac{2 \\, r}{{\\ell}}\\right)} - y^{2} e^{\\left(\\frac{2 \\, r}{{\\ell}}\\right)} - {\\ell}^{2}\\right)} e^{\\left(-\\frac{r}{{\\ell}}\\right)}}{2 \\, {\\ell}}, x e^{\\frac{r}{{\\ell}}}, y e^{\\frac{r}{{\\ell}}}\\right) \\end{array}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\begin{array}{llcl} \\Phi:& \\mathcal{M}_{\\rm P} & \\longrightarrow & \\mathbb{R}^{2,3} \\\\ & \\left(t, x, y, r\\right) & \\longmapsto & \\left(U, V, X, Y, Z\\right) = \\left(\\frac{{\\left({\\ell}^{2} e^{\\left(\\frac{2 \\, r}{{\\ell}}\\right)} - t^{2} e^{\\left(\\frac{2 \\, r}{{\\ell}}\\right)} + x^{2} e^{\\left(\\frac{2 \\, r}{{\\ell}}\\right)} + y^{2} e^{\\left(\\frac{2 \\, r}{{\\ell}}\\right)} + {\\ell}^{2}\\right)} e^{\\left(-\\frac{r}{{\\ell}}\\right)}}{2 \\, {\\ell}}, t e^{\\frac{r}{{\\ell}}}, -\\frac{{\\left({\\ell}^{2} e^{\\left(\\frac{2 \\, r}{{\\ell}}\\right)} + t^{2} e^{\\left(\\frac{2 \\, r}{{\\ell}}\\right)} - x^{2} e^{\\left(\\frac{2 \\, r}{{\\ell}}\\right)} - y^{2} e^{\\left(\\frac{2 \\, r}{{\\ell}}\\right)} - {\\ell}^{2}\\right)} e^{\\left(-\\frac{r}{{\\ell}}\\right)}}{2 \\, {\\ell}}, x e^{\\frac{r}{{\\ell}}}, y e^{\\frac{r}{{\\ell}}}\\right) \\end{array}$$" ], "text/plain": [ "Phi: MP → R23\n", " (t, x, y, r) ↦ (U, V, X, Y, Z) = (1/2*(l^2*e^(2*r/l) - t^2*e^(2*r/l) + x^2*e^(2*r/l) + y^2*e^(2*r/l) + l^2)*e^(-r/l)/l, t*e^(r/l), -1/2*(l^2*e^(2*r/l) + t^2*e^(2*r/l) - x^2*e^(2*r/l) - y^2*e^(2*r/l) - l^2)*e^(-r/l)/l, x*e^(r/l), y*e^(r/l))" ] }, "execution_count": 114, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Phi.restrict(MP).display(X_Poinc_exp, X23)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Summary\n", "\n", "10 charts have been defined on the $\\mathrm{AdS}_4$ spacetime:" ] }, { "cell_type": "code", "execution_count": 115, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left[\\left(\\mathcal{M}_0,({\\tau}, {\\rho}, {\\theta}, {\\phi})\\right), \\left(\\mathcal{M}_0,({\\tau}, {x_\\rho}, {y_\\rho}, {z_\\rho})\\right), \\left(\\mathcal{M}_0,({\\tau}, R, {\\theta}, {\\phi})\\right), \\left(\\mathcal{M}_0,({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi})\\right), \\left(\\mathcal{M}_{\\rm P},({\\tau}, {\\rho}, {\\theta}, {\\phi})\\right), \\left(\\mathcal{M}_{\\rm P},({\\tau}, R, {\\theta}, {\\phi})\\right), \\left(\\mathcal{M}_{\\rm P},({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi})\\right), \\left(\\mathcal{M}_{\\rm P},(t, x, y, u)\\right), \\left(\\mathcal{M}_{\\rm P},(t, x, y, z)\\right), \\left(\\mathcal{M}_{\\rm P},(t, x, y, r)\\right)\\right]\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left[\\left(\\mathcal{M}_0,({\\tau}, {\\rho}, {\\theta}, {\\phi})\\right), \\left(\\mathcal{M}_0,({\\tau}, {x_\\rho}, {y_\\rho}, {z_\\rho})\\right), \\left(\\mathcal{M}_0,({\\tau}, R, {\\theta}, {\\phi})\\right), \\left(\\mathcal{M}_0,({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi})\\right), \\left(\\mathcal{M}_{\\rm P},({\\tau}, {\\rho}, {\\theta}, {\\phi})\\right), \\left(\\mathcal{M}_{\\rm P},({\\tau}, R, {\\theta}, {\\phi})\\right), \\left(\\mathcal{M}_{\\rm P},({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi})\\right), \\left(\\mathcal{M}_{\\rm P},(t, x, y, u)\\right), \\left(\\mathcal{M}_{\\rm P},(t, x, y, z)\\right), \\left(\\mathcal{M}_{\\rm P},(t, x, y, r)\\right)\\right]$$" ], "text/plain": [ "[Chart (M_0, (ta, rh, th, ph)),\n", " Chart (M_0, (ta, x_rho, y_rho, z_rho)),\n", " Chart (M_0, (ta, R, th, ph)),\n", " Chart (M_0, (tat, ch, th, ph)),\n", " Chart (MP, (ta, rh, th, ph)),\n", " Chart (MP, (ta, R, th, ph)),\n", " Chart (MP, (tat, ch, th, ph)),\n", " Chart (MP, (t, x, y, u)),\n", " Chart (MP, (t, x, y, z)),\n", " Chart (MP, (t, x, y, r))]" ] }, "execution_count": 115, "metadata": {}, "output_type": "execute_result" } ], "source": [ "M.atlas()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are actually 7 main charts, the other ones being subcharts:" ] }, { "cell_type": "code", "execution_count": 116, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left[\\left(\\mathcal{M}_0,({\\tau}, {\\rho}, {\\theta}, {\\phi})\\right), \\left(\\mathcal{M}_0,({\\tau}, {x_\\rho}, {y_\\rho}, {z_\\rho})\\right), \\left(\\mathcal{M}_0,({\\tau}, R, {\\theta}, {\\phi})\\right), \\left(\\mathcal{M}_0,({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi})\\right), \\left(\\mathcal{M}_{\\rm P},(t, x, y, u)\\right), \\left(\\mathcal{M}_{\\rm P},(t, x, y, z)\\right), \\left(\\mathcal{M}_{\\rm P},(t, x, y, r)\\right)\\right]\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\left[\\left(\\mathcal{M}_0,({\\tau}, {\\rho}, {\\theta}, {\\phi})\\right), \\left(\\mathcal{M}_0,({\\tau}, {x_\\rho}, {y_\\rho}, {z_\\rho})\\right), \\left(\\mathcal{M}_0,({\\tau}, R, {\\theta}, {\\phi})\\right), \\left(\\mathcal{M}_0,({\\tilde{\\tau}}, {\\chi}, {\\theta}, {\\phi})\\right), \\left(\\mathcal{M}_{\\rm P},(t, x, y, u)\\right), \\left(\\mathcal{M}_{\\rm P},(t, x, y, z)\\right), \\left(\\mathcal{M}_{\\rm P},(t, x, y, r)\\right)\\right]$$" ], "text/plain": [ "[Chart (M_0, (ta, rh, th, ph)),\n", " Chart (M_0, (ta, x_rho, y_rho, z_rho)),\n", " Chart (M_0, (ta, R, th, ph)),\n", " Chart (M_0, (tat, ch, th, ph)),\n", " Chart (MP, (t, x, y, u)),\n", " Chart (MP, (t, x, y, z)),\n", " Chart (MP, (t, x, y, r))]" ] }, "execution_count": 116, "metadata": {}, "output_type": "execute_result" } ], "source": [ "M.top_charts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Except for $(\\tau,x_\\rho,y_\\rho,z_\\rho)$ (which has been introduced only for graphical purposes), the expression of the metric tensor in each of these chart is" ] }, { "cell_type": "code", "execution_count": 117, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}g = -\\cosh\\left({\\rho}\\right)^{2} \\mathrm{d} {\\tau}\\otimes \\mathrm{d} {\\tau} + {\\ell}^{2} \\mathrm{d} {\\rho}\\otimes \\mathrm{d} {\\rho} + {\\ell}^{2} \\sinh\\left({\\rho}\\right)^{2} \\mathrm{d} {\\theta}\\otimes \\mathrm{d} {\\theta} + {\\ell}^{2} \\sin\\left({\\theta}\\right)^{2} \\sinh\\left({\\rho}\\right)^{2} \\mathrm{d} {\\phi}\\otimes \\mathrm{d} {\\phi}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}g = -\\cosh\\left({\\rho}\\right)^{2} \\mathrm{d} {\\tau}\\otimes \\mathrm{d} {\\tau} + {\\ell}^{2} \\mathrm{d} {\\rho}\\otimes \\mathrm{d} {\\rho} + {\\ell}^{2} \\sinh\\left({\\rho}\\right)^{2} \\mathrm{d} {\\theta}\\otimes \\mathrm{d} {\\theta} + {\\ell}^{2} \\sin\\left({\\theta}\\right)^{2} \\sinh\\left({\\rho}\\right)^{2} \\mathrm{d} {\\phi}\\otimes \\mathrm{d} {\\phi}$$" ], "text/plain": [ "g = -cosh(rh)^2 dta⊗dta + l^2 drh⊗drh + l^2*sinh(rh)^2 dth⊗dth + l^2*sin(th)^2*sinh(rh)^2 dph⊗dph" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}g = \\left( -\\frac{R^{2} + {\\ell}^{2}}{{\\ell}^{2}} \\right) \\mathrm{d} {\\tau}\\otimes \\mathrm{d} {\\tau} + \\left( \\frac{{\\ell}^{2}}{R^{2} + {\\ell}^{2}} \\right) \\mathrm{d} R\\otimes \\mathrm{d} R + R^{2} \\mathrm{d} {\\theta}\\otimes \\mathrm{d} {\\theta} + R^{2} \\sin\\left({\\theta}\\right)^{2} \\mathrm{d} {\\phi}\\otimes \\mathrm{d} {\\phi}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}g = \\left( -\\frac{R^{2} + {\\ell}^{2}}{{\\ell}^{2}} \\right) \\mathrm{d} {\\tau}\\otimes \\mathrm{d} {\\tau} + \\left( \\frac{{\\ell}^{2}}{R^{2} + {\\ell}^{2}} \\right) \\mathrm{d} R\\otimes \\mathrm{d} R + R^{2} \\mathrm{d} {\\theta}\\otimes \\mathrm{d} {\\theta} + R^{2} \\sin\\left({\\theta}\\right)^{2} \\mathrm{d} {\\phi}\\otimes \\mathrm{d} {\\phi}$$" ], "text/plain": [ "g = -(R^2 + l^2)/l^2 dta⊗dta + l^2/(R^2 + l^2) dR⊗dR + R^2 dth⊗dth + R^2*sin(th)^2 dph⊗dph" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}g = -\\frac{{\\ell}^{2}}{\\cos\\left({\\chi}\\right)^{2}} \\mathrm{d} {\\tilde{\\tau}}\\otimes \\mathrm{d} {\\tilde{\\tau}} + \\frac{{\\ell}^{2}}{\\cos\\left({\\chi}\\right)^{2}} \\mathrm{d} {\\chi}\\otimes \\mathrm{d} {\\chi} + \\frac{{\\ell}^{2} \\sin\\left({\\chi}\\right)^{2}}{\\cos\\left({\\chi}\\right)^{2}} \\mathrm{d} {\\theta}\\otimes \\mathrm{d} {\\theta} + \\frac{{\\ell}^{2} \\sin\\left({\\chi}\\right)^{2} \\sin\\left({\\theta}\\right)^{2}}{\\cos\\left({\\chi}\\right)^{2}} \\mathrm{d} {\\phi}\\otimes \\mathrm{d} {\\phi}\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}g = -\\frac{{\\ell}^{2}}{\\cos\\left({\\chi}\\right)^{2}} \\mathrm{d} {\\tilde{\\tau}}\\otimes \\mathrm{d} {\\tilde{\\tau}} + \\frac{{\\ell}^{2}}{\\cos\\left({\\chi}\\right)^{2}} \\mathrm{d} {\\chi}\\otimes \\mathrm{d} {\\chi} + \\frac{{\\ell}^{2} \\sin\\left({\\chi}\\right)^{2}}{\\cos\\left({\\chi}\\right)^{2}} \\mathrm{d} {\\theta}\\otimes \\mathrm{d} {\\theta} + \\frac{{\\ell}^{2} \\sin\\left({\\chi}\\right)^{2} \\sin\\left({\\theta}\\right)^{2}}{\\cos\\left({\\chi}\\right)^{2}} \\mathrm{d} {\\phi}\\otimes \\mathrm{d} {\\phi}$$" ], "text/plain": [ "g = -l^2/cos(ch)^2 dtat⊗dtat + l^2/cos(ch)^2 dch⊗dch + l^2*sin(ch)^2/cos(ch)^2 dth⊗dth + l^2*sin(ch)^2*sin(th)^2/cos(ch)^2 dph⊗dph" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}g = -\\frac{u^{2}}{{\\ell}^{2}} \\mathrm{d} t\\otimes \\mathrm{d} t + \\frac{u^{2}}{{\\ell}^{2}} \\mathrm{d} x\\otimes \\mathrm{d} x + \\frac{u^{2}}{{\\ell}^{2}} \\mathrm{d} y\\otimes \\mathrm{d} y + \\frac{{\\ell}^{2}}{u^{2}} \\mathrm{d} u\\otimes \\mathrm{d} u\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}g = -\\frac{u^{2}}{{\\ell}^{2}} \\mathrm{d} t\\otimes \\mathrm{d} t + \\frac{u^{2}}{{\\ell}^{2}} \\mathrm{d} x\\otimes \\mathrm{d} x + \\frac{u^{2}}{{\\ell}^{2}} \\mathrm{d} y\\otimes \\mathrm{d} y + \\frac{{\\ell}^{2}}{u^{2}} \\mathrm{d} u\\otimes \\mathrm{d} u$$" ], "text/plain": [ "g = -u^2/l^2 dt⊗dt + u^2/l^2 dx⊗dx + u^2/l^2 dy⊗dy + l^2/u^2 du⊗du" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}g = -\\frac{{\\ell}^{2}}{z^{2}} \\mathrm{d} t\\otimes \\mathrm{d} t + \\frac{{\\ell}^{2}}{z^{2}} \\mathrm{d} x\\otimes \\mathrm{d} x + \\frac{{\\ell}^{2}}{z^{2}} \\mathrm{d} y\\otimes \\mathrm{d} y + \\frac{{\\ell}^{2}}{z^{2}} \\mathrm{d} z\\otimes \\mathrm{d} z\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}g = -\\frac{{\\ell}^{2}}{z^{2}} \\mathrm{d} t\\otimes \\mathrm{d} t + \\frac{{\\ell}^{2}}{z^{2}} \\mathrm{d} x\\otimes \\mathrm{d} x + \\frac{{\\ell}^{2}}{z^{2}} \\mathrm{d} y\\otimes \\mathrm{d} y + \\frac{{\\ell}^{2}}{z^{2}} \\mathrm{d} z\\otimes \\mathrm{d} z$$" ], "text/plain": [ "g = -l^2/z^2 dt⊗dt + l^2/z^2 dx⊗dx + l^2/z^2 dy⊗dy + l^2/z^2 dz⊗dz" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\\[\\newcommand{\\Bold}[1]{\\mathbf{#1}}g = -e^{\\left(\\frac{2 \\, r}{{\\ell}}\\right)} \\mathrm{d} t\\otimes \\mathrm{d} t + e^{\\left(\\frac{2 \\, r}{{\\ell}}\\right)} \\mathrm{d} x\\otimes \\mathrm{d} x + e^{\\left(\\frac{2 \\, r}{{\\ell}}\\right)} \\mathrm{d} y\\otimes \\mathrm{d} y +\\mathrm{d} r\\otimes \\mathrm{d} r\\]" ], "text/latex": [ "$$\\newcommand{\\Bold}[1]{\\mathbf{#1}}g = -e^{\\left(\\frac{2 \\, r}{{\\ell}}\\right)} \\mathrm{d} t\\otimes \\mathrm{d} t + e^{\\left(\\frac{2 \\, r}{{\\ell}}\\right)} \\mathrm{d} x\\otimes \\mathrm{d} x + e^{\\left(\\frac{2 \\, r}{{\\ell}}\\right)} \\mathrm{d} y\\otimes \\mathrm{d} y +\\mathrm{d} r\\otimes \\mathrm{d} r$$" ], "text/plain": [ "g = -e^(2*r/l) dt⊗dt + e^(2*r/l) dx⊗dx + e^(2*r/l) dy⊗dy + dr⊗dr" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for chart in M.top_charts():\n", " if chart is not X_hyp_graph:\n", " show(g.display(chart))" ] }, { "cell_type": "code", "execution_count": 118, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total elapsed time: 318.7865767090043 s\n" ] } ], "source": [ "print(\"Total elapsed time: {} s\".format(time.perf_counter() - comput_time0))" ] } ], "metadata": { "kernelspec": { "display_name": "SageMath 9.4", "language": "sage", "name": "sagemath" }, "language": "python", "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 1 }