ChETEC_school/GCE Lab 2 - Solar Composition - Stellar Yields and Isotopes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GCE Lab 2 - Solar Composition - Stellar Yields and Isotopes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, you will analyze the contributions of three enrichment sources on the isotopic composition of the Sun:\n",
    "* Massive stars ( > 8-10 M$_\\odot$) - Stellar winds and core-collapse explosions,\n",
    "* Low-mass asymptotic-giant-branch (AGB) stars ( < 8-10 M$_\\odot$),\n",
    "* Type Ia supernovae (SNe Ia) - Exploding white dwarfs, which are the remnant of low-mass stars.\n",
    "\n",
    "You will be able to change the stellar yields for AGB and massive stars.\n",
    "* **Option 1**\n",
    "    * AGB and massive stars: [Ritter et al. (2018, NuGrid)](https://ui.adsabs.harvard.edu/abs/2018MNRAS.480..538R/abstract)\n",
    "    \n",
    "    \n",
    "* **Option 2**\n",
    "    * AGB stars: [Cristallo et al. (2015)](https://ui.adsabs.harvard.edu/abs/2015ApJS..219...40C/abstract)\n",
    "    * Massive stars: [Limongi & Chieffi (2018)](https://ui.adsabs.harvard.edu/abs/2018ApJS..237...13L/abstract), mix of rotation velocities\n",
    "    \n",
    "    \n",
    "* **Option 3**\n",
    "    * AGB stars: [Cristallo et al. (2015)](https://ui.adsabs.harvard.edu/abs/2015ApJS..219...40C/abstract)\n",
    "    * Massive stars: [Nomoto et al. (2013)](https://ui.adsabs.harvard.edu/abs/2013ARA%26A..51..457N/abstract)\n",
    "\n",
    "\n",
    "* All SN Ia yields are from [Iwamoto et al. (1999)](https://ui.adsabs.harvard.edu/abs/1999ApJS..125..439I/abstract)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the standard Python packages\n",
    "import copy\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Two-zone galactic chemical evolution code\n",
    "import JINAPyCEE.omega_plus as omega_plus\n",
    "\n",
    "# Run scripts for this notebook\n",
    "%run script_solar_ab.py\n",
    "kwargs[\"ns_merger_on\"] = False\n",
    "kwargs[\"imf_yields_range\"] = [1.0, 40.0]\n",
    "\n",
    "# Matplotlib option\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run GCE code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using yield_tables/agb_and_massive_stars_nugrid_MESAonly_fryer12delay.txt\n",
      "OMEGA+ run in progress..\n",
      "OMEGA run in progress..\n",
      "   OMEGA run completed - Run time: 2.01s\n",
      "   OMEGA+ run completed - Run time: 2.57s\n"
     ]
    }
   ],
   "source": [
    "# \\\\\\\\\\\\\\\\\\\\ Modify below \\\\\\\\\\\\\\\\\\\\\\\\\n",
    "# ====================================\n",
    "\n",
    "# Choose one yield option (uncomment one \"kwargs[\"table\"]\" line)\n",
    "\n",
    "# NuGrid (Ritter et al. 2018) for both AGB and massive stars\n",
    "kwargs[\"table\"] = \"yield_tables/agb_and_massive_stars_nugrid_MESAonly_fryer12delay.txt\"\n",
    "\n",
    "# Cristallo et al. (2015) for AGB stars\n",
    "# Limongi & Chieffi (2018) for massive stars\n",
    "#kwargs[\"table\"] = \"yield_tables/agb_and_massive_stars_C15_LC18_R_mix.txt\"\n",
    "\n",
    "# Cristallo et al. (2015) for AGB stars\n",
    "# Nomoto et al. (2013) for massive stars\n",
    "#kwargs[\"table\"] = \"yield_tables/agb_and_massive_stars_C15_N13_0_5_HNe.txt\"\n",
    "\n",
    "# ====================================\n",
    "# ////////// Modify above ////////////\n",
    "\n",
    "# Run the GCE code OMEGA+\n",
    "print(\"Using\", kwargs[\"table\"])\n",
    "op = omega_plus.omega_plus(**kwargs)\n",
    "\n",
    "# Get the timestep where the metallicity is close to Solar\n",
    "if kwargs[\"table\"] == \"yield_tables/agb_and_massive_stars_nugrid_MESAonly_fryer12delay.txt\":\n",
    "    ii_Sun = i_t_Sun + 5\n",
    "else:\n",
    "    ii_Sun = copy.deepcopy(i_t_Sun)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the Isotopic Contributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot below is normalized to the Solar composition of [Asplund et al. (2009)](https://ui.adsabs.harvard.edu/abs/2009ARA%26A..47..481A/abstract). If the numerical predictions are above 1.0, they overestimate the abundance derived for the Sun. If they are lower, they underestimate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'X / X$_\\\\odot$')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# \\\\\\\\\\\\\\\\\\\\ Modify below \\\\\\\\\\\\\\\\\\\\\\\\\n",
    "# ====================================\n",
    "\n",
    "# Select the isotopes you want to analyze\n",
    "# It can also be a mix of isotopes and elements\n",
    "# Example 1: species = [\"C-12\", \"O-16\", Fe-56\"]\n",
    "# Example 2: species = [\"C-12\", \"C-13\", \"C\", \"Si-28\"]\n",
    "species = [\"C-12\", \"O-16\", \"Fe-56\", \"Zr-94\"]\n",
    "\n",
    "# ====================================\n",
    "# ////////// Modify above ////////////\n",
    "\n",
    "# Set the figure size\n",
    "fig = plt.figure(figsize=(8,5.0))\n",
    "matplotlib.rcParams.update({'font.size': 16.0})\n",
    "\n",
    "# Plot contributions\n",
    "op.inner.mass_frac_plot(species=species, cycle=ii_Sun, yscale=\"linear\")\n",
    "\n",
    "# Increase size labels\n",
    "plt.title(\"\")\n",
    "ax = plt.gca()\n",
    "for tick in ax.xaxis.get_major_ticks():\n",
    "    tick.label.set_fontsize(18)\n",
    "plt.ylabel(\"X / X$_\\odot$\", fontsize=18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* If predictions **reach 1** on the y axis, you reproduced the Solar abundance for that isotope or element.\n",
    "* If predictions are **above 1** on the y axis, you synthesized too much.\n",
    "* If predictions are **below 1** on the y axis, you did not synthesize enough."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Explore different isotopes and look at the impact of using different yields tables. Stellar yields are a fundamental source of uncertainties in chemical evolution studies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}