Identify cluster members in omega Centauri with Gaia DR2 in jdaviz

jdaviz_gaia_cmd.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e465df10-53b2-4c9a-8794-9404db92d554",
   "metadata": {},
   "source": [
    "# omega Centauri\n",
    "\n",
    "For a thorough analysis of omega Cen with Gaia EDR3, see [Soltis, Casertano & Riess (2021)](https://ui.adsabs.harvard.edu/abs/2021ApJ...908L...5S/abstract)\n",
    "\n",
    "**Outline**\n",
    "\n",
    "1. Load Gaia source catalog near the center of omega Centauri \n",
    "2. Load a JWST NIRcam image in the same location\n",
    "3. Open the image and catalog in jdaviz\n",
    "4. Plot a color-magnitude diagram\n",
    "5. Plot the proper motions (to ID likely cluster members)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32ea5ec7-9664-4d93-924d-6f8e2739e7c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import astropy.units as u\n",
    "from astropy.coordinates import SkyCoord, Distance\n",
    "from astroquery.mast import MastMissions, Catalogs\n",
    "\n",
    "import jdaviz as jd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee8264b8-e7ab-40f4-b863-f10af8e28fb9",
   "metadata": {},
   "source": [
    "Query for Gaia DR2 sources near the center of omega Centauri."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a536f8b5-e9f0-4780-a6be-c5e0e3cb1f3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "target = SkyCoord(ra=201.69959, dec=-47.47732, unit=u.deg)\n",
    "\n",
    "catalog = Catalogs.query_region(\n",
    "    target,\n",
    "    radius=0.03,\n",
    "    catalog=\"gaia\",\n",
    "    version=2\n",
    ")\n",
    "\n",
    "# # Note: more sources are available via Gaia TAP from DR3, though the server is flaky\n",
    "# from astroquery.gaia import Gaia\n",
    "# Gaia.MAIN_GAIA_TABLE = \"gaiadr3.gaia_source\"\n",
    "# r = Gaia.query_object_async(\n",
    "#     coordinate=target, \n",
    "#     radius='0.03 deg'\n",
    "# )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7f72f6e-4c72-4a40-8249-e91088d151af",
   "metadata": {},
   "source": [
    "Calculate the absolute magnitudes and colors of the Gaia sources, add corresponding columns to the catalog table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6bcb68cf-faec-47b4-992d-168506a999ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "parallax = catalog['parallax'] * u.milliarcsecond\n",
    "distance = Distance(parallax=parallax, allow_negative=True)\n",
    "\n",
    "g_mag = catalog['phot_g_mean_mag']\n",
    "bp_mag = catalog['phot_bp_mean_mag']\n",
    "rp_mag = catalog['phot_rp_mean_mag']\n",
    "\n",
    "# Gaia color:\n",
    "bp_minus_rp = bp_mag - rp_mag\n",
    "\n",
    "# absolute magnitude in G:\n",
    "G_abs_mag = g_mag - 5 * (np.log10(distance.to_value(u.parsec)) - 1)\n",
    "\n",
    "# add the new quantities to the table:\n",
    "catalog['G_abs_mag'] = G_abs_mag\n",
    "catalog['bp-rp'] = bp_minus_rp\n",
    "catalog['distance'] = distance.to_value(u.kpc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2435d489-237a-4d16-b558-1c0b4fa55ed9",
   "metadata": {},
   "source": [
    "For those who love a labeled color magnitude diagram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "260777d3-9f24-4cd6-9b77-44f119844fc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "ax = plt.gca()\n",
    "ax.scatter(bp_minus_rp, G_abs_mag, marker='.')\n",
    "ax.annotate(\"ω Cen RGB\", (1.8, -8))  # red giant branch\n",
    "ax.annotate(\"?\", (0.4, -2))  # blue stragglers\n",
    "ax.annotate(\"(not in DR2 query result)\", (0.6, 15))  # blue stragglers\n",
    "ax.axhspan(10, 25, alpha=0.2, color='gray')\n",
    "ax.set(\n",
    "    xlabel='$\\\\rm B_p - R_p$',\n",
    "    ylabel='$\\\\rm M_G$',\n",
    "    xlim=[0, 2.2],\n",
    "    ylim=[25, -15],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a64633d0-4e38-4d0e-8987-4e0e2c1f547e",
   "metadata": {},
   "source": [
    "Load a JWST NIRCam image into jdaviz near the center of the catalog query. File size: 126 MB."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3dfae36-3387-4403-88f8-f3c7a29f935d",
   "metadata": {},
   "outputs": [],
   "source": [
    "jd.load('mast:jwst/products/jw04343-o002_t001_nircam_clear-f444w_i2d.fits', format='Image')\n",
    "\n",
    "orientation = jd.plugins['Orientation']\n",
    "orientation.align_by = 'WCS'\n",
    "# jd.load('mast:jwst/products/jw04343-o002_t001_nircam_clear-f444w_cat.ecsv', format='Catalog')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "893d27f9-0de1-422b-9706-5eea3bb971f6",
   "metadata": {},
   "source": [
    "Load the source catalog into jdaviz, including all columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8631055-db99-4ba8-993e-72f112aa20b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the catalog from an astropy Table\n",
    "ldr = jd.loaders['object']\n",
    "ldr.object = catalog\n",
    "ldr.format = 'Catalog'\n",
    "\n",
    "# import *all* columns from the table\n",
    "ldr.importer.col_other.select_all()\n",
    "\n",
    "ldr.load()\n",
    "\n",
    "jd.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10f06b42-9c17-4bb7-8dfb-835b0f9c4b9c",
   "metadata": {},
   "source": [
    "You can rearrange the viewers by dragging their tabs.\n",
    "\n",
    "Create a new scatter viewer for a color magnitude diagram, adjust the plot options:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f99605e3-0936-4046-959b-a48d70d9e16a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new scatter viewer:\n",
    "create_scatter_viewer = jd.new_viewers['Scatter']\n",
    "create_scatter_viewer.dataset = 'Catalog'\n",
    "scatter_viewer = create_scatter_viewer()\n",
    "\n",
    "# change the x, y values of the scatter plot\n",
    "plot_opts = jd.plugins['Plot Options']\n",
    "plot_opts.viewer = 'Scatter'\n",
    "plot_opts.xatt = 'bp-rp'\n",
    "plot_opts.yatt = 'G_abs_mag'\n",
    "\n",
    "# change marker styles\n",
    "plot_opts.viewer = 'Scatter'\n",
    "plot_opts.marker_size = 30\n",
    "\n",
    "# to map marker colors to a third catalog column (effective temperature)\n",
    "plot_opts.marker_color_mode = 'Linear'\n",
    "plot_opts.marker_color_col = 'teff_val'\n",
    "plot_opts.marker_colormap = 'viridis'\n",
    "plot_opts.marker_colormap_vmin = 4000\n",
    "plot_opts.marker_colormap_vmax = 6000\n",
    "\n",
    "\n",
    "# Make image marker shape = open circles\n",
    "plot_opts.viewer = 'Image'\n",
    "plot_opts.marker_fill = False\n",
    "\n",
    "# set the viewer limits\n",
    "scatter_viewer.set_limits(\n",
    "    x_min=0, x_max=2.5,\n",
    "    y_min=15, y_max=-10\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9081442-3ff1-4f0a-a855-a69eaa87086c",
   "metadata": {},
   "source": [
    "Create another scatter viewer, this time for proper motions to identify cluster members:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27c57d20-0e68-45fb-89c1-f88e5f1f8c26",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new scatter viewer:\n",
    "create_scatter_viewer.dataset = 'Catalog'\n",
    "scatter_viewer_pm = create_scatter_viewer()\n",
    "\n",
    "# change the x, y values of the scatter plot\n",
    "plot_opts = jd.plugins['Plot Options']\n",
    "plot_opts.viewer = 'Scatter (1)'\n",
    "plot_opts.xatt = 'pmra'\n",
    "plot_opts.yatt = 'pmdec'\n",
    "\n",
    "# change marker styles\n",
    "plot_opts.viewer = 'Scatter (1)'\n",
    "plot_opts.marker_size = 30"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cafbeb0-9a01-42fd-9cb7-8086b1da21bc",
   "metadata": {},
   "source": [
    "Optionally add a histogram viewer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98efe5e1-1c30-45b3-883c-887a19ae64f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create_hist_viewer = jd.new_viewers['Histogram']\n",
    "# create_hist_viewer.dataset = 'Catalog'\n",
    "# create_hist_viewer.xatt = 'teff_val'\n",
    "# hist_viewer = create_hist_viewer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9511a96b-3882-4142-a2b5-becc7bc0f1d8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.13.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}