{ "cells": [ { "cell_type": "markdown", "id": "052f183f", "metadata": {}, "source": [ "# Phenomenological Models for the Global 21-cm Signal" ] }, { "cell_type": "markdown", "id": "72b88425", "metadata": {}, "source": [ "Two common phenomenological parameterizations for the global 21-cm signal are included in *ARES* and get their own set of pre-defined parameters: the tanh and Gaussian models. To generate them (without default parameters) one need only do:" ] }, { "cell_type": "code", "execution_count": 1, "id": "afc94f23", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n", "# Loaded $ARES/input/inits/inits_planck_TTTEEE_lowl_lowE_best.txt.\n", "# Loaded $ARES/input/inits/inits_planck_TTTEEE_lowl_lowE_best.txt.\n", "\n", "############################################################################\n", "## ARES Simulation: Overview ##\n", "############################################################################\n", "Phenomenological model! Not much to report...\n", "############################################################################\n", "# Loaded $ARES/input/inits/inits_planck_TTTEEE_lowl_lowE_best.txt.\n", "\n", "############################################################################\n", "## ARES Simulation: Overview ##\n", "############################################################################\n", "Phenomenological model! Not much to report...\n", "############################################################################\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%pylab inline\n", "import ares\n", "import numpy as np\n", "import matplotlib.pyplot as pl\n", " \n", "sim_1 = ares.simulations.Global21cm(tanh_model=True)\n", "sim_2 = ares.simulations.Global21cm(gaussian_model=True)\n", " \n", "# Have a look\n", "ax, zax = sim_1.GlobalSignature(color='k', fig=1)\n", "ax, zax = sim_2.GlobalSignature(color='b', ax=ax)" ] }, { "cell_type": "markdown", "id": "fa67ebbd", "metadata": {}, "source": [ "Now, you might say \"I could have done that myself extremely easily.\" You'd be right! However, sometimes there's an advantage in working through *ARES* even when using simply parametric forms for the global 21-cm signal. For example, you can tap into *ARES*' inference module and fit data, perform forecasting, or run large sets of models. In each of these applications, *ARES* can take care of some annoying things for you, like tracking the quantities you care about and saving them to disk in a format that can be easily analyzed later on. For more concrete examples, check out the following pages:\n", "\n", "* [Example: Inline Analysis](example_inline_analysis)\n", "* [Example: MCMC Global Signal](example_mcmc_gs)\n", "* [Example: MC Sampling](example_mc_sampling)\n", "* [Example: MCMC Analysis](example_mcmc_analysis)\n", "\n", "In the remaining sections we'll cover different ways to parameterize the signal." ] }, { "cell_type": "markdown", "id": "c4410ac5", "metadata": {}, "source": [ "### Parameterizing the IGM " ] }, { "cell_type": "markdown", "id": "cbb22087", "metadata": {}, "source": [ "Whereas the Gaussian absorption model makes no link between the brightness temperature and the underlying quantities of interest (ionization history, etc.), the tanh model first models $J_{\\alpha}(z)$, $T_K(z)$, and $x_i(z)$, and from those histories produces $\\delta T_b(z)$.\n", "\n", "Now, let's assemble a set of parameters that will generate a global 21-cm signal using ParameterizedQuantity objects for each main piece: the thermal, ionization, and Ly-$\\alpha$ histories. We'll assume that the thermal and ionization histories are *tanh* functions, but take the Ly-$\\alpha$ background evolution to be a power-law in redshift:\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "8a69e8c5", "metadata": {}, "outputs": [], "source": [ "pars = \\\n", " {\n", " 'problem_type': 100, # blank slate global 21-cm signal problem\n", " 'parametric_model': True, # in lieu of, e.g., tanh_model=True\n", " \n", " # Lyman alpha history first: ParameterizedQuantity #0\n", " 'pop_Ja': 'pq[0]',\n", " 'pq_func[0]': 'pl', # Ja(z) = p0 * ((1 + z) / p1)**p2\n", " 'pq_func_var[0]': '1+z',\n", " 'pq_func_par0[0]': 1e-9,\n", " 'pq_func_par1[0]': 20.,\n", " 'pq_func_par2[0]': -7.,\n", " \n", " # Thermal history: ParameterizedQuantity #1\n", " 'pop_Tk': 'pq[1]', # Tk(z) = p1 + (p0 - p1) * 0.5 * (1 + tanh((p2 - z) / p3))\n", " 'pq_func[1]': 'tanh_abs',\n", " 'pq_func_var[1]': 'z',\n", " 'pq_func_par0[1]': 1e3,\n", " 'pq_func_par1[1]': 0.,\n", " 'pq_func_par2[1]': 8.,\n", " 'pq_func_par3[1]': 6.,\n", " \n", " # Ionization history: ParameterizedQuantity #2\n", " 'pop_xi': 'pq[2]', # xi(z) = p1 + (p0 - p1) * 0.5 * (1 + tanh((p2 - z) / p3))\n", " 'pq_func[2]': 'tanh_abs',\n", " 'pq_func_var[2]': 'z',\n", " 'pq_func_par0[2]': 1,\n", " 'pq_func_par1[2]': 0.,\n", " 'pq_func_par2[2]': 8.,\n", " 'pq_func_par3[2]': 2.,\n", " }" ] }, { "cell_type": "markdown", "id": "c97f3368", "metadata": {}, "source": [ "**NOTE:** The thermal history automatically includes the adiabatic cooling term, so users need not add account for that explicitly.\n", "\n", "To run it, as always:" ] }, { "cell_type": "code", "execution_count": 3, "id": "4ad36cbb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# Loaded $ARES/input/inits/inits_planck_TTTEEE_lowl_lowE_best.txt.\n", "# Loaded $ARES/input/inits/inits_planck_TTTEEE_lowl_lowE_best.txt.\n", "\n", "############################################################################\n", "## ARES Simulation: Overview ##\n", "############################################################################\n", "Phenomenological model! Not much to report...\n", "############################################################################\n" ] }, { "data": { "text/plain": [ "(,\n", " )" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sim_3 = ares.simulations.Global21cm(**pars)\n", "sim_3.GlobalSignature(color='r', ax=ax)" ] }, { "cell_type": "markdown", "id": "2a5dee0b", "metadata": {}, "source": [ "Now, because the parameters of these models are hard to intuit ahead of time, it can be useful to run a set of them. As per usual, we can use some built-in machinery." ] }, { "cell_type": "code", "execution_count": 4, "id": "1df1bde5", "metadata": {}, "outputs": [], "source": [ "blob_pars = ares.util.BlobBundle('gs:basics') \\\n", " + ares.util.BlobBundle('gs:history')\n", "\n", "base_pars = pars.copy()\n", "base_pars.update(blob_pars)\n", " \n", "mg = ares.inference.ModelGrid(**base_pars)" ] }, { "cell_type": "markdown", "id": "7c896b13", "metadata": {}, "source": [ "Let's focus on the $J_{\\alpha}(z)$ parameters:" ] }, { "cell_type": "code", "execution_count": 5, "id": "2fd2336a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting 143-element model grid.\n", "Running 143-element model grid.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "grid: 100% |################################################| Time: 0:00:18 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Processor 0: Wrote test_Ja_pl.*.pkl (Tue Feb 8 14:52:54 2022)\n", "Calculation complete: Tue Feb 8 14:52:54 2022\n", "Elapsed time (min) : 0.309\n" ] } ], "source": [ "mg.axes = {'pq_func_par1[0]': np.arange(15, 26, 1), \n", " 'pq_func_par2[0]': np.arange(-9, -2.5, 0.5)}\n", " \n", "mg.run('test_Ja_pl', clobber=True)" ] }, { "cell_type": "markdown", "id": "630fa32a", "metadata": {}, "source": [ "and a quick plot:" ] }, { "cell_type": "code", "execution_count": 6, "id": "df60b0b9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "############################################################################\n", "## Analysis: Model Set ##\n", "############################################################################\n", "## ---------------------------------------------------------------------- ##\n", "## Basic Information ##\n", "## ---------------------------------------------------------------------- ##\n", "## path : ./ ##\n", "## prefix : test_Ja_pl ##\n", "## N-d : 2 ##\n", "## ---------------------------------------------------------------------- ##\n", "## param #00: pq_func_par1[0] ##\n", "## param #01: pq_func_par2[0] ##\n", "############################################################################\n", "\n", "# Loaded test_Ja_pl.000.blob_0d.z_C.pkl\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "anl = ares.analysis.ModelSet('test_Ja_pl')\n", " \n", "anl.Scatter(anl.parameters, c='z_C', fig=4, edgecolors='none')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 5 }