{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Redshift Space Distortions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial follows on from the [coeval cube](coeval_cubes) and [lightcone](lightcones) tutorials, and provides an introduction to apply the effects of redshift space distortions (RSDs) with ``21cmFAST``. If you are new to ``21cmFAST`` you should go through the coeval cube and lightcone tutorials first." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "According to [Mao et al. 2012](https://arxiv.org/pdf/1104.2094), there are two types of distortions due to peculiar velocity along the line-of-sight (LOS) vecotr $\\mathbf n$.\n", "1. **Brightness temperature distortion in real-space.** This effect modifies the 21-cm optical depth due to the velocity gradient of HI atoms,\n", "\\begin{equation*}\n", "\\tau_{21}\\propto\\left|1+\\frac{1}{H}\\frac{d\\left(\\mathbf n\\cdot \\mathbf v\\right)}{d\\left(\\mathbf n\\cdot \\mathbf x\\right)}\\right|^{-1}.\n", "\\end{equation*}\n", " This velocity gradient arises simply because we measure the optical depth from an expanding medium.\n", "2. **Apparent location distortion in redshift-space.** This is the well known RSD effect: sources appear to be closer/further away from us due to their peculiar velocity. In our case, the sources are the HI atoms!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above two effects can be accounted when running lightcones in `21cmFAST`, regardless if they are rectilinear or angular. The first effect is taken into account by setting `include_dvdr_in_tau21=True`, while the second effect can be taken into account by setting `apply_rsds=True`.\n", "\n", "While we stress that the first effect (that is controlled by the `include_dvdr_in_tau21` flag) has nothing to with redshift space, below we collectively refer to it together with the second effect as \"RSDs\"." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2023-03-16T17:09:54.467808Z", "start_time": "2023-03-16T17:09:53.102218Z" } }, "outputs": [], "source": [ "from tempfile import mkdtemp\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "import py21cmfast as p21c\n", "from py21cmfast import plotting" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "cache = p21c.OutputCache(mkdtemp())" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using v4.0.0b1.dev312+gb6f5204f.d20250728 of 21cmFAST\n" ] } ], "source": [ "print(f\"Using v{p21c.__version__} of 21cmFAST\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Redshift Space Distortions in Rectilinear Lightcone" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's set up a standard rectilinear lightcone subclass." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "inputs = p21c.InputParameters.from_template(\n", " ['simple', 'small'],\n", " node_redshifts=p21c.wrapper.inputs.get_logspaced_redshifts(\n", " min_redshift=7.0,\n", " z_step_factor=1.1,\n", " max_redshift=12.0,\n", " ),\n", " random_seed=1234,\n", " N_THREADS=4\n", ")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "lcn = p21c.RectilinearLightconer.between_redshifts(\n", " min_redshift=inputs.node_redshifts[-1]+0.5,\n", " max_redshift=inputs.node_redshifts[0]-0.5,\n", " quantities=(\"brightness_temp\",\"density\"),\n", " resolution=inputs.simulation_options.cell_size,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that we defined the boundaries of our lightconer to be slightly shorter than the boundaries of `node_redshifts`. This is required when we `apply_rsds`, as \"mass\" outside the lightcone shifts to its interior due to the RSD effect. `21cmFASTv4` accounts for this effect by constructing a bigger \"ghost\" lightconer that is used throughout the simulation. Once the RSD calculation finishes, the returned lightcone object has the same size as the user's input lightconer.\n", "\n", "Note also that the implementation of RSDs on lightcones in `21cmFASTv4` has been updated. In `21cmFASTv3` both RSD effects were implemented on the coeval box, while the lightcone was later constructed from interpolating the coeval boxes. In contrast, in `21cmFASTv4` the coeval boxes are always computed without the effect of RSDs, as these effects are applied later on the lightcone once its construction is complete. This was done to highlight the fact that RSDs act along the LOS, and since coeval boxes have no preferred direction, the lightcone is the more appropriate object to consider here. Nevertheless, `21cmFASTv4` still allows the application of RSDs on coeval boxes (even though it is less consistent), as we show later in this tutorial." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We run first a lightcone without any RSD effect." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2020-02-29T22:25:11.665270Z", "start_time": "2020-02-29T22:25:08.941967Z" }, "scrolled": true }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b7f0fa116437427b92519f36abac217b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n" ], "text/plain": [] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7c9dc7fb0a594b48b1b62f29e95046a9", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n" ], "text/plain": [] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lightcone = p21c.run_lightcone(\n", " lightconer=lcn,\n", " inputs=inputs,\n", " cache=cache,\n", " progressbar=True,\n", " include_dvdr_in_tau21=False,\n", " apply_rsds=False,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we run two more lightcones with the RSD effects." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "85ed9f46eac84daa97b8542ba5526661", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n" ], "text/plain": [] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f0587ea5b505468b9381780889085b9a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n" ], "text/plain": [] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lightcone_include_dvdr_in_tau21 = p21c.run_lightcone(\n", " lightconer=lcn,\n", " inputs=inputs,\n", " cache=cache,\n", " progressbar=True,\n", " include_dvdr_in_tau21=True,\n", " apply_rsds=False,\n", ")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7f47604aa3ee4a628b73f7dfadd45278", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n" ], "text/plain": [] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0c8f19eb644c4a45a2845d8ab3c783b5", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n" ], "text/plain": [] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lightcone_apply_rsds = p21c.run_lightcone(\n", " lightconer=lcn,\n", " inputs=inputs,\n", " cache=cache,\n", " progressbar=True,\n", " include_dvdr_in_tau21=True,\n", " apply_rsds=True,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before making any comparison plots, let us verify that the boundaries of the lightcone from the last simulation matches the boundaries of our input lightconer, even though the inclusion of the RSDs required a larger \"ghost\" lightcone to consider." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "np.True_" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.all(lightcone_apply_rsds.lightcone_distances == lcn.lc_distances)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's compare all the three lightcones!\n", "\n", "We begin by showing the front face of them. Note that when setting `include_dvdr_in_tau21=True`, then the lightcone box stored in `brightness_temp` is modified to include the effect of velocity gradient. If `apply_rsds=True`, the output lightcone contains a new quantity named `brightness_temp_with_rsds`. As the name suggests, this quantity accounts for the effects of `apply_rsds` on the brightness temperature field such that this quantity is now given in redshift space. In real space, the brightness temperature field is still saved in `brightness_temp`." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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