diff --git a/notebooks/MAST/exploring_jwst_transmission/comparing_spectral_types_jwst.ipynb b/notebooks/MAST/exploring_jwst_transmission/comparing_spectral_types_jwst.ipynb new file mode 100644 index 00000000..78361568 --- /dev/null +++ b/notebooks/MAST/exploring_jwst_transmission/comparing_spectral_types_jwst.ipynb @@ -0,0 +1,1059 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "65f3eaf0", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "id": "5d84656f", + "metadata": {}, + "source": [ + "# Comparing Stellar Spectral Types with JWST Data" + ] + }, + { + "cell_type": "markdown", + "id": "515b6fba", + "metadata": {}, + "source": [ + "***" + ] + }, + { + "cell_type": "markdown", + "id": "ea13b8cc", + "metadata": {}, + "source": [ + "# Learning Goals\n", + "\n", + "By the end of this tutorial, you will:\n", + "\n", + "- Be able to extract data for multiple targets that meet `SpectralDB` search criteria.\n", + "- Become familiar with cross-referencing outputs with `SpectralDB` against `astroquery` catalogs.\n", + "- Compare the strength of spectral features in G and K stars by eye." + ] + }, + { + "cell_type": "markdown", + "id": "d28853a0", + "metadata": {}, + "source": [ + "# Introduction\n", + "With the new (as of the creation of this notebook) JWST observations rapidly being acquired, accessing and making sense of the new data is crucial for many observational astronomers. Checking that our astronomical intuition holds with new datasets is a helpful way to make sure that we understand the properties of these datasets.\n", + "\n", + "\n", + "One concrete, applicable idea from stellar astronomy is that spectral features should vary as a function of stellar type. In particular, [stellar spectral classification](https://lweb.cfa.harvard.edu/~pberlind/atlas/htmls/note.html#:~:text=Each%20spectral%20type%20is%20divided,%22color%22%20and%20surface%20brightness.) holds that as stars get cooler, they will have stronger molecular spectral features. This is because at cooler temperatures, molecules are less readily broken apart by thermal motions. Because the abundance of molecules is higher in cooler stars, these molecules will absorb more radiation, creating deeper absorption lines. \n", + "\n", + "In this tutorial, we will leverage the unique capabilities of `SpectralDB`, a MAST tool, to search directly on wavelength, querying across observations to compare the spectra of different stellar types. Specifically, we will compare the spectrum of a G star to a cooler K star, identifying the stronger molecular absorption expected in the K star.\n", + "\n", + "For more information on using Specviz and SpectralDB, see the notebook [link previously written SpectralDB notebook].\n", + "\n", + "The workflow for this notebook consists of:\n", + "\n", + "- [Collating targets](#Collating-targets)\n", + "- [Downloading spectra](#Downloading-spectra)\n", + "- [Plotting spectra](#Plotting-spectra)" + ] + }, + { + "cell_type": "markdown", + "id": "9253f3d4", + "metadata": {}, + "source": [ + "# Imports\n", + "\n", + "- *matplotlib* to visualize the downloaded JWST data.\n", + "- *sys* to report our Python version.\n", + "- *requests* to interact with the SpectralDB API.\n", + "- *numpy* to rework the downloaded JWST data.\n", + "- *warnings* to limit the number of warnings printed.\n", + "- *astroquery* to cross-reference our stars." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0f1a0b6a", + "metadata": { + "ExecuteTime": { + "end_time": "2022-09-23T04:53:37.986303Z", + "start_time": "2022-09-23T04:53:37.976121Z" + } + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import requests\n", + "import sys\n", + "import numpy as np\n", + "import warnings\n", + "\n", + "from astroquery.simbad import Simbad" + ] + }, + { + "cell_type": "markdown", + "id": "2b7782b1", + "metadata": {}, + "source": [ + "## Collating targets" + ] + }, + { + "cell_type": "markdown", + "id": "1ba13cf2", + "metadata": {}, + "source": [ + "Let's first submit a request via the `SpectralDB` API. As shown in the [SpectralDB documentation](https://mast.stsci.edu/spectra/docs/), these requests can be made over flux and wavelength. There are a number of molecular features blueward of 2 microns, so let's set our wavelength range from 0 to 2 microns." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ac013ae6", + "metadata": { + "ExecuteTime": { + "end_time": "2022-09-23T04:53:39.482130Z", + "start_time": "2022-09-23T04:53:39.470002Z" + } + }, + "outputs": [], + "source": [ + "wav_range = '0,2'" + ] + }, + { + "cell_type": "markdown", + "id": "d10f812a", + "metadata": {}, + "source": [ + "As demonstrated in [link other notebook], these conditions will be passed to the `SpectralDB` request as strings. Ranges are represented as comma-separated values, as above.\n", + "\n", + "To make some minimum quality cut on our observations (and reduce the time to query our observations), we'll also enforce that our observations have a minimum of 1 Jansky." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "314a423e", + "metadata": { + "ExecuteTime": { + "end_time": "2022-09-23T04:53:39.911252Z", + "start_time": "2022-09-23T04:53:39.903327Z" + } + }, + "outputs": [], + "source": [ + "min_flux = '1'" + ] + }, + { + "cell_type": "markdown", + "id": "34539530", + "metadata": {}, + "source": [ + "We can now submit our request, starting with the `base_url` upon which `SpectralDB` requests are constructed:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "780e7abc", + "metadata": { + "ExecuteTime": { + "end_time": "2022-09-23T04:53:40.495639Z", + "start_time": "2022-09-23T04:53:40.487193Z" + } + }, + "outputs": [], + "source": [ + "base_url = 'https://mast.stsci.edu/spectra/api/v0.1/search'" + ] + }, + { + "cell_type": "markdown", + "id": "8861fc22", + "metadata": {}, + "source": [ + "Now we create our dictionary of conditions. Note that the syntax for requesting a quantity above some minimum value in `SpectralDB` is `[quantity].gt: min_value` (where gt stands for \"greater than\")." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a66ef317", + "metadata": { + "ExecuteTime": { + "end_time": "2022-09-23T04:53:40.655866Z", + "start_time": "2022-09-23T04:53:40.651025Z" + } + }, + "outputs": [], + "source": [ + "conditions = {'flux.gt': min_flux, 'wavelength': wav_range}" + ] + }, + { + "cell_type": "markdown", + "id": "7d79e81a", + "metadata": {}, + "source": [ + "With the `conditions` and `base_url` set, we can submit and read in our request." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "72d96756", + "metadata": { + "ExecuteTime": { + "end_time": "2022-09-23T04:53:42.141032Z", + "start_time": "2022-09-23T04:53:40.819042Z" + } + }, + "outputs": [], + "source": [ + "# submit the request\n", + "response = requests.post(base_url, json={'conditions': conditions,\n", + " 'columns': ['targetName']})\n", + "\n", + "# turn the response into a readable dictionary\n", + "response_data = response.json()" + ] + }, + { + "cell_type": "markdown", + "id": "0faf9d9b", + "metadata": {}, + "source": [ + "`response_data` is now a Python `dict` that we can parse to access the data we'd like. To get a sense for the data, let's take a look at the first entry of the value of the `results` key." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "455ad439", + "metadata": { + "ExecuteTime": { + "end_time": "2022-09-23T04:53:42.197510Z", + "start_time": "2022-09-23T04:53:42.142292Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'fileName': 'jw01120-o005_t002_nirspec_prism-clear_x1d.fits',\n", + " 'x': 0,\n", + " 'y': None,\n", + " 'wavelength': 0.5984808741858381,\n", + " 'flux': 2659528056.691551,\n", + " 'targetName': 'HWK-I 74230'}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "response_data['results'][0]" + ] + }, + { + "cell_type": "markdown", + "id": "c0136bdc", + "metadata": {}, + "source": [ + "The response of the request returned the fileneame of the data, the average wavelength of the observation, the average flux of the observation, and the name of the target name observed. For 2D data, `x` and `y` are used for plotting, but they are not relevant to the 1D spectra that we consider here.\n", + "\n", + "Next, we can parse through these results to construct a list of unique target names. We'll ignore blank target names." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "5cc341e0", + "metadata": { + "ExecuteTime": { + "end_time": "2022-09-23T04:53:42.221734Z", + "start_time": "2022-09-23T04:53:42.198378Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['(2002 XV93)' '10 Hygiea' '175113 (2004 PF115)' '2 Pallas'\n", + " '230965 (2004 XA192)' '2MASS J12560183-1257276' '2MASS J15395077-3404566'\n", + " '2MASS J16194609+5534178' '2MASS J17430448+6655015'\n", + " '2MASS J17540383-2810466' '307982 (2004 PG115)' '52872 Okyrhoe'\n", + " '624 Hektor' 'BD+04 3653' 'ENCELADUS' 'F2M1106' 'GANYMEDE' 'GCRV 21765'\n", + " 'GSPC P330-E' 'HWK-I 74230' 'IRAS 05248-7007' 'JUPITER' 'Kopff'\n", + " 'NGC 2070 S7B' 'NGC 7469' 'NIRISS Focus Field' 'ORIBAR-NIRSPEC' 'Read'\n", + " 'SDSSJ1652+1728' 'SDSSJ1723+3411' 'SMACS J0723.3-7327' 'SMP-LMC-58'\n", + " 'TITAN' 'TYC 3986-834-1' 'TYC 4433-1800-1' 'VV114' 'WD1657+343' 'XID2028']\n" + ] + } + ], + "source": [ + "names = []\n", + "for d in response_data['results']:\n", + " name = d['targetName']\n", + " if name != '':\n", + " names += [name]\n", + " \n", + "print(np.unique(names))" + ] + }, + { + "cell_type": "markdown", + "id": "dd7f1116", + "metadata": { + "ExecuteTime": { + "end_time": "2022-09-23T04:40:38.974661Z", + "start_time": "2022-09-23T04:40:38.963267Z" + } + }, + "source": [ + "These targets span a number of different catalogs. We can use `astroquery`'s functionality to check the spectral type of each object, if it is a star.\n", + "\n", + "First, we instantiate a [`Simbad` object](https://astroquery.readthedocs.io/en/latest/simbad/simbad.html) and make sure that the spectral type is reported in its queries." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "303bff35", + "metadata": { + "ExecuteTime": { + "end_time": "2022-09-23T04:53:42.663971Z", + "start_time": "2022-09-23T04:53:42.653208Z" + } + }, + "outputs": [], + "source": [ + "customSimbad = Simbad()\n", + "customSimbad.add_votable_fields('sptype')" + ] + }, + { + "cell_type": "markdown", + "id": "59f40948", + "metadata": {}, + "source": [ + "Next, we iterate through the objects and report out their stellar types. Because we've added the `sptype` field to our request, the `query_object` method that we will use will throw a warning for objects that are not stars. Therefore, we will disable warnings for while we iterate through the objects." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "74c7b98c", + "metadata": { + "ExecuteTime": { + "end_time": "2022-09-23T04:53:42.895088Z", + "start_time": "2022-09-23T04:53:42.812770Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2MASS J12560183-1257276 L8.0\n", + "2MASS J15395077-3404566 M0III\n", + "2MASS J16194609+5534178 G0-5\n", + "2MASS J17430448+6655015 A5V\n", + "2MASS J17540383-2810466 M4.5III\n", + "BD+04 3653 K5III\n", + "GCRV 21765 A0\n", + "GSPC P330-E G2V\n", + "TYC 3986-834-1 K0III\n", + "TYC 4433-1800-1 A3V\n", + "WD1657+343 DA.9\n" + ] + } + ], + "source": [ + "with warnings.catch_warnings():\n", + " # disable warnings\n", + " warnings.simplefilter(\"ignore\")\n", + " for name in np.unique(names):\n", + "\n", + " # query the object\n", + " result = customSimbad.query_object(name)\n", + "\n", + " # ignore this object if no result is returned\n", + " if not result:\n", + " continue\n", + "\n", + " spectral_type = result['SP_TYPE'][0]\n", + "\n", + " # ignore this object if it has no spectral type\n", + " if spectral_type == '':\n", + " continue\n", + "\n", + " print(name, spectral_type)" + ] + }, + { + "cell_type": "markdown", + "id": "74ce5c36", + "metadata": {}, + "source": [ + "It seems that our sample of targets includes a K star (BD+04 3653) and a G star (GSPC P330-E). Their spectra should be different; K dwarfs, for instance, should have more pronounced molecular features than G dwarfs." + ] + }, + { + "cell_type": "markdown", + "id": "27b38906", + "metadata": {}, + "source": [ + "## Downloading spectra" + ] + }, + { + "cell_type": "markdown", + "id": "9e501582", + "metadata": {}, + "source": [ + "Let's start by examining the G star in our `response_data`." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "cc6ad63a", + "metadata": { + "ExecuteTime": { + "end_time": "2022-09-23T04:53:44.090260Z", + "start_time": "2022-09-23T04:53:44.069665Z" + } + }, + "outputs": [], + "source": [ + "# access target files for G star\n", + "\n", + "targetname = 'GSPC P330-E'\n", + "\n", + "targetfiles = []\n", + "\n", + "for d in response_data['results']:\n", + " if d['targetName'] == targetname:\n", + " targetfiles += [d['fileName']]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "e08ad3d6", + "metadata": { + "ExecuteTime": { + "end_time": "2022-09-23T04:57:39.130258Z", + "start_time": "2022-09-23T04:57:39.115552Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['jw01538-o062_t002_nirspec_g140h-f100lp_x1d.fits',\n", + " 'jw01538-o062_t002_nirspec_g140m-f100lp_x1d.fits',\n", + " 'jw01538-o062_t002_nirspec_g235h-f170lp_x1d.fits',\n", + " 'jw01538-o062_t002_nirspec_g235m-f170lp_x1d.fits'], dtype='" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "markers, caps, bars = plt.errorbar(k_star_wav_masked, np.log10(k_star_flux_masked), \n", + " yerr=k_star_log_err, \n", + " color='teal',\n", + " fmt='.',\n", + " ms=1,\n", + " elinewidth=1,\n", + " ecolor='blue')\n", + "\n", + "[bar.set_alpha(0.1) for bar in bars]\n", + "[cap.set_alpha(0.1) for cap in caps]\n", + "\n", + "\n", + "plt.title('K star', fontsize=25)\n", + "\n", + "plt.xlabel('Wavelength (microns)', fontsize=25)\n", + "\n", + "plt.ylabel('Log10 Flux (Jy)', fontsize=25)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "388f623f", + "metadata": { + "ExecuteTime": { + "end_time": "2022-09-23T04:56:53.450768Z", + "start_time": "2022-09-23T04:56:53.339384Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/zg/pp98nf5j52b0_pww3l0b71f40000gq/T/ipykernel_39107/480626688.py:2: RuntimeWarning: invalid value encountered in log10\n", + " markers, caps, bars = plt.errorbar(g_star_wav_masked, np.log10(g_star_flux_masked),\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Log10 Flux (Jy)')" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "markers, caps, bars = plt.errorbar(g_star_wav_masked, np.log10(g_star_flux_masked), \n", + " yerr=(1/np.log(10)) * g_star_flux_err_masked/g_star_flux_masked, \n", + " color='teal',\n", + " fmt='.',\n", + " ms=1,\n", + " elinewidth=1,\n", + " ecolor='blue')\n", + "\n", + "[bar.set_alpha(0.1) for bar in bars]\n", + "[cap.set_alpha(0.1) for cap in caps]\n", + "\n", + "plt.title('G star', fontsize=25)\n", + "\n", + "plt.xlabel('Wavelength (microns)', fontsize=25)\n", + "\n", + "plt.ylabel('Log10 Flux (Jy)', fontsize=25)" + ] + }, + { + "cell_type": "markdown", + "id": "0cac04ae", + "metadata": {}, + "source": [ + "Showing both spectra in the same plot will make the spectral differences more distinct." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "58a48b5b", + "metadata": { + "ExecuteTime": { + "end_time": "2022-09-23T04:57:21.016404Z", + "start_time": "2022-09-23T04:57:20.687601Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/zg/pp98nf5j52b0_pww3l0b71f40000gq/T/ipykernel_39107/1665948621.py:4: RuntimeWarning: invalid value encountered in log10\n", + " markers, caps, bars = plt.errorbar(k_star_wav_masked, np.log10(k_star_flux_masked),\n", + "/var/folders/zg/pp98nf5j52b0_pww3l0b71f40000gq/T/ipykernel_39107/1665948621.py:17: RuntimeWarning: invalid value encountered in log10\n", + " markers, caps, bars = plt.errorbar(g_star_wav_masked, np.log10(g_star_flux_masked),\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Log10 Flux (Jy)')" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "\n", + "\n", + "markers, caps, bars = plt.errorbar(k_star_wav_masked, np.log10(k_star_flux_masked), \n", + " yerr=(1/np.log(10)) * k_star_flux_err_masked/k_star_flux_masked, \n", + " color='teal',\n", + " fmt='.',\n", + " ms=4,\n", + " elinewidth=1,\n", + " ecolor='blue', label='K star')\n", + "\n", + "[bar.set_alpha(0.1) for bar in bars]\n", + "[cap.set_alpha(0.1) for cap in caps]\n", + "\n", + "\n", + "\n", + "markers, caps, bars = plt.errorbar(g_star_wav_masked, np.log10(g_star_flux_masked), \n", + " yerr=(1/np.log(10)) * g_star_flux_err_masked/g_star_flux_masked, \n", + " color='black',\n", + " fmt='.',\n", + " ms=4,\n", + " elinewidth=1,\n", + " ecolor='gray',\n", + " label='G star')\n", + "\n", + "[bar.set_alpha(0.5) for bar in bars]\n", + "[cap.set_alpha(0.5) for cap in caps]\n", + "\n", + "# plt.scatter(wav, flux)\n", + "\n", + "# plt.yscale('log')\n", + "plt.ylim(5,7.5)\n", + "plt.xlim(2.5, 2.7) \n", + "\n", + "plt.legend(fontsize=18)\n", + "plt.xlabel('Wavelength (microns)', fontsize=25)\n", + "\n", + "plt.ylabel('Log10 Flux (Jy)', fontsize=25)" + ] + }, + { + "cell_type": "markdown", + "id": "75ffc285", + "metadata": {}, + "source": [ + "As expected, the K star has stronger spectral features (deeper absorption lines) than the G star. While noise is defeinitely present (identified by deviations from the mean that are not coherent across multiple pixels), this noise is defeinitely low enough that spectral features (such as near 2.55 microns in the K star spectrum) can be seen by eye." + ] + }, + { + "cell_type": "markdown", + "id": "1ddfaa71", + "metadata": {}, + "source": [ + "# Exercises\n", + "- Compare an A star spectrum to a G star spectrum. How do the spectral features differ? Do they differ in expected ways?\n", + "- Find observations of Jupiter's emission spectrum over a wide wavelength range (e.g., 1 to 15 micron). Some spectral features are obvious by eye; what are they?" + ] + }, + { + "cell_type": "markdown", + "id": "bf351b95", + "metadata": {}, + "source": [ + "# Additional Resources\n", + "- [Classification of stellar spectra](http://www.star.ucl.ac.uk/~pac/spectral_classification.html) \n", + "- [JWST home page](https://webb.nasa.gov/)\n", + "- [NIRSPEC filters and gratings](https://jwst-docs.stsci.edu/jwst-near-infrared-spectrograph/nirspec-instrumentation/nirspec-dispersers-and-filters)" + ] + }, + { + "cell_type": "markdown", + "id": "4f799c59", + "metadata": {}, + "source": [ + "# About this Notebook\n", + "\n", + "If you have questions, comments, or feedback about this notebook, please email archive@stsci.edu.\n", + "\n", + "**Last updated**: 2022-09-22" + ] + }, + { + "cell_type": "markdown", + "id": "1a9681aa", + "metadata": {}, + "source": [ + "# Citations\n", + "If you use `astroquery`, `matplotlib`, or `numpy` for published research, please cite the authors. Follow these links for more information about citing `astoquery`, `matplotlib`, and `numpy`:\n", + "\n", + "* [Citing `astroqery`](https://github.com/astropy/astroquery/blob/main/astroquery/CITATION)\n", + "* [Citing `matplotlib`](https://matplotlib.org/stable/users/project/citing.html)\n", + "* [Citing `numpy`](https://numpy.org/citing-numpy/)" + ] + }, + { + "cell_type": "markdown", + "id": "6ec567e1", + "metadata": {}, + "source": [ + "[Top of Page](#top)\n", + "\"Space\n" + ] + } + ], + "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": "3.10.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/MAST/exploring_jwst_transmission/comparing_spectral_types_jwst_solutions.ipynb b/notebooks/MAST/exploring_jwst_transmission/comparing_spectral_types_jwst_solutions.ipynb new file mode 100644 index 00000000..14e27d2a --- /dev/null +++ b/notebooks/MAST/exploring_jwst_transmission/comparing_spectral_types_jwst_solutions.ipynb @@ -0,0 +1,665 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7c5bb4c8", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "id": "733b48b4", + "metadata": {}, + "source": [ + "# Comparing Stellar Spectral Types with JWST Data: solutions" + ] + }, + { + "cell_type": "markdown", + "id": "f8414d58", + "metadata": {}, + "source": [ + "***" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "9715972c", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import requests\n", + "import sys\n", + "import numpy as np\n", + "import warnings\n", + "\n", + "from astroquery.simbad import Simbad" + ] + }, + { + "cell_type": "markdown", + "id": "bef554c4", + "metadata": {}, + "source": [ + "## Exercise 1\n", + "Compare an A star spectrum to a G star spectrum. How do the spectral features differ? Do they differ in expected ways?" + ] + }, + { + "cell_type": "markdown", + "id": "1bd7139c", + "metadata": {}, + "source": [ + "We'll begin by collating the same observations as the previous notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "0acd879c", + "metadata": {}, + "outputs": [], + "source": [ + "wav_range = '0,2'\n", + "min_flux = '1'\n", + "base_url = 'https://mast.stsci.edu/spectra/api/v0.1/search'\n", + "conditions = {'flux.gt': min_flux, 'wavelength': wav_range}\n", + "# submit the request\n", + "response = requests.post(base_url, json={'conditions': conditions,\n", + " 'columns': ['targetName']})\n", + "\n", + "# turn the response into a readable dictionary\n", + "response_data = response.json()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "0760d8a0", + "metadata": {}, + "outputs": [], + "source": [ + "names = []\n", + "for d in response_data['results']:\n", + " name = d['targetName']\n", + " if name != '':\n", + " names += [name]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "5f5cdb92", + "metadata": {}, + "outputs": [], + "source": [ + "customSimbad = Simbad()\n", + "customSimbad.add_votable_fields('sptype')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "5724ae95", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2MASS J12560183-1257276 L8.0\n", + "2MASS J15395077-3404566 M0III\n", + "2MASS J16194609+5534178 G0-5\n", + "2MASS J17430448+6655015 A5V\n", + "2MASS J17540383-2810466 M4.5III\n", + "BD+04 3653 K5III\n", + "GCRV 21765 A0\n", + "GSPC P330-E G2V\n", + "TYC 3986-834-1 K0III\n", + "TYC 4433-1800-1 A3V\n", + "WD1657+343 DA.9\n" + ] + } + ], + "source": [ + "with warnings.catch_warnings():\n", + " # disable warnings\n", + " warnings.simplefilter(\"ignore\")\n", + " for name in np.unique(names):\n", + "\n", + " # query the object\n", + " result = customSimbad.query_object(name)\n", + "\n", + " # ignore this object if no result is returned\n", + " if not result:\n", + " continue\n", + "\n", + " spectral_type = result['SP_TYPE'][0]\n", + "\n", + " # ignore this object if it has no spectral type\n", + " if spectral_type == '':\n", + " continue\n", + "\n", + " print(name, spectral_type)" + ] + }, + { + "cell_type": "markdown", + "id": "f77fe652", + "metadata": {}, + "source": [ + "Let's download the G star from the main notebook first." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "667f9fdd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Successfully found data for jw01538-o062_t002_nirspec_g235h-f170lp_x1d.fits\n" + ] + } + ], + "source": [ + "# access target files for G star\n", + "\n", + "targetname = 'GSPC P330-E'\n", + "\n", + "targetfiles = []\n", + "\n", + "for d in response_data['results']:\n", + " if d['targetName'] == targetname:\n", + " targetfiles += [d['fileName']]\n", + "\n", + " \n", + "# retrieve our system's Python version for the request. \n", + "version = \".\".join(map(str, sys.version_info[:3]))\n", + "# create HTTP Header Variables\n", + "headers = {\"Content-type\": \"application/x-www-form-urlencoded\",\n", + " \"Accept\": \"text/plain\",\n", + " \"User-agent\":\"python-requests/\"+version}\n", + "\n", + "# download first target file\n", + "targetfile = np.unique(targetfiles)[2]\n", + "\n", + "request_url=f'https://mast.stsci.edu/spectra/api/v0.1/retrieve?filename={targetfile}' \n", + "\n", + "# perform the HTTP request\n", + "spectrum_response = requests.get(request_url, headers=headers)\n", + "\n", + "spectrum_object = spectrum_response.json()\n", + "\n", + "print(spectrum_object['message'])\n", + "\n", + "# extract G star spectrum components\n", + "\n", + "g_star_wav = np.array(spectrum_object['data']['wavelength'])\n", + "\n", + "g_star_flux = np.array(spectrum_object['data']['flux'])\n", + "g_star_flux_err = np.array(spectrum_object['data']['fluxErr'])\n", + "\n", + "# remove noisy G star spectral data\n", + "g_star_flux_masked = g_star_flux[g_star_flux_err/g_star_flux < 2]\n", + "g_star_flux_err_masked = g_star_flux_err[g_star_flux_err/g_star_flux < 2]\n", + "g_star_wav_masked = g_star_wav[g_star_flux_err/g_star_flux < 2]\n", + "\n", + "# transform G star spectral data into log space\n", + "g_star_log_err = (1/np.log(10)) * g_star_flux_err_masked/g_star_flux_masked" + ] + }, + { + "cell_type": "markdown", + "id": "be2109ce", + "metadata": {}, + "source": [ + "Now, let's examine the data for the star TYC 4433-1800-1." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "e06144ca", + "metadata": {}, + "outputs": [], + "source": [ + "targetname = '2MASS J17430448+6655015'\n", + "targetfiles = []\n", + "\n", + "for d in response_data['results']:\n", + " if d['targetName'] == targetname:\n", + " targetfiles += [d['fileName']]\n", + "\n", + "\n", + "targetfile = np.unique(targetfiles)[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "de6dac7a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['jw01118-o003_t001_nirspec_g140m-f100lp_x1d.fits',\n", + " 'jw01118-o006_t001_nirspec_g140m-f100lp_x1d.fits'], dtype='" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "\n", + "\n", + "markers, caps, bars = plt.errorbar(a_star_wav_masked, np.log10(a_star_flux_masked), \n", + " yerr=a_star_log_err, \n", + " color='teal',\n", + " fmt='.',\n", + " ms=4,\n", + " elinewidth=1,\n", + " ecolor='blue', label='A star')\n", + "\n", + "[bar.set_alpha(0.1) for bar in bars]\n", + "[cap.set_alpha(0.1) for cap in caps]\n", + "\n", + "\n", + "\n", + "markers, caps, bars = plt.errorbar(g_star_wav_masked, np.log10(g_star_flux_masked), \n", + " yerr=g_star_log_err, \n", + " color='black',\n", + " fmt='.',\n", + " ms=4,\n", + " elinewidth=1,\n", + " ecolor='gray',\n", + " label='G star')\n", + "\n", + "[bar.set_alpha(0.5) for bar in bars]\n", + "[cap.set_alpha(0.5) for cap in caps]\n", + "\n", + "# plt.scatter(wav, flux)\n", + "\n", + "# plt.yscale('log')\n", + "# plt.ylim(5,7.5)\n", + "# plt.xlim(2.5, 2.7) \n", + "\n", + "plt.legend(fontsize=18)\n", + "plt.xlabel('Wavelength (microns)', fontsize=25)\n", + "\n", + "plt.ylabel('Log10 Flux (Jy)', fontsize=25)" + ] + }, + { + "cell_type": "markdown", + "id": "20a6c7b0", + "metadata": {}, + "source": [ + "Both stars have a continuum slope over wavelength, and there are some features in the A star spectrum, some of which could be caused by hydrogen lines. These spectral features are much less strong than the G stars spectral feature, as expected — A stars tend to be fast rotators with few spectral features that are strongly rotationally broadened." + ] + }, + { + "cell_type": "markdown", + "id": "b90dd770", + "metadata": {}, + "source": [ + "## Exercise 2\n", + "Find observations of Jupiter's emission spectrum over a wide wavelength range (e.g., 1 to 15 micron). Some spectral features are obvious by eye; what are they?" + ] + }, + { + "cell_type": "markdown", + "id": "6fdd6c99", + "metadata": {}, + "source": [ + "This time, we'll make a request based on target name instead of wavelength." + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "id": "f070b359", + "metadata": {}, + "outputs": [], + "source": [ + "min_flux = '0'" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "id": "df3fefa2", + "metadata": {}, + "outputs": [], + "source": [ + "conditions = {'targetName':'JUPITER', 'wavelength': '8,13','flux.gt': min_flux}" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "id": "8e7a1816", + "metadata": {}, + "outputs": [], + "source": [ + "# submit the request\n", + "response = requests.post(base_url, json={'conditions': conditions,\n", + " 'columns': ['targetName']})\n", + "\n", + "# turn the response into a readable dictionary\n", + "response_data = response.json()" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "id": "e5d3fd4b", + "metadata": {}, + "outputs": [], + "source": [ + "targetfiles = []\n", + "for d in response_data['results']:\n", + " targetfiles += [d['fileName']]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "id": "aa16ec51", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['jw01022-o021_t001_miri_ch3-short_x1d.fits',\n", + " 'jw01022-o022_t001_miri_ch2-short_x1d.fits',\n", + " 'jw01022-o022_t001_miri_ch3-short_x1d.fits',\n", + " 'jw01022-o023_t001_miri_ch2-short_x1d.fits',\n", + " 'jw01022-o023_t001_miri_ch3-short_x1d.fits',\n", + " 'jw01022-o024_t001_miri_ch2-short_x1d.fits',\n", + " 'jw01022-o024_t001_miri_ch3-short_x1d.fits',\n", + " 'jw01022-o025_t001_miri_ch3-short_x1d.fits',\n", + " 'jw01022-o026_t001_miri_ch3-short_x1d.fits',\n", + " 'jw01246-c1001_t001_miri_ch2-shortmediumlong_x1d.fits',\n", + " 'jw01246-c1001_t001_miri_ch3-shortmediumlong_x1d.fits',\n", + " 'jw01246-o004_t002_miri_ch2-longshortmedium-_x1d.fits',\n", + " 'jw01246-o004_t002_miri_ch2-shortmediumlong_x1d.fits',\n", + " 'jw01246-o004_t002_miri_ch3-longshortmedium-_x1d.fits',\n", + " 'jw01246-o004_t002_miri_ch3-shortmediumlong_x1d.fits',\n", + " 'jw01246-o051_t004_miri_ch2-short_x1d.fits',\n", + " 'jw01246-o051_t004_miri_ch3-short_x1d.fits',\n", + " 'jw01246-o052_t001_miri_ch2-shortmediumlong_x1d.fits',\n", + " 'jw01246-o052_t001_miri_ch3-shortmediumlong_x1d.fits',\n", + " 'jw01246-o053_t003_miri_ch2-shortmediumlong_x1d.fits',\n", + " 'jw01246-o053_t003_miri_ch3-shortmediumlong_x1d.fits'],\n", + " dtype='" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# download first target file\n", + "targetfile = np.unique(targetfiles)[2]\n", + "targetfile2 = np.unique(targetfiles)[9]\n", + "\n", + "request_url=f'https://mast.stsci.edu/spectra/api/v0.1/retrieve?filename={targetfile}' \n", + "\n", + "# perform the HTTP request\n", + "spectrum_response = requests.get(request_url, headers=headers)\n", + "\n", + "spectrum_object = spectrum_response.json()\n", + "\n", + "# extract G star spectrum components\n", + "\n", + "wav1 = np.array(spectrum_object['data']['wavelength'])\n", + "\n", + "flux1 = np.array(spectrum_object['data']['flux'])\n", + "\n", + "request_url=f'https://mast.stsci.edu/spectra/api/v0.1/retrieve?filename={targetfile2}' \n", + "\n", + "# perform the HTTP request\n", + "spectrum_response = requests.get(request_url, headers=headers)\n", + "\n", + "spectrum_object = spectrum_response.json()\n", + "\n", + "# extract G star spectrum components\n", + "\n", + "wav2 = np.array(spectrum_object['data']['wavelength'])\n", + "\n", + "flux2 = np.array(spectrum_object['data']['flux'])\n", + "\n", + "plt.figure()\n", + "plt.plot(wav1, flux1, color='teal', lw=1)\n", + "plt.plot(wav2, flux2, color='black')\n", + "plt.yscale('log')\n", + "plt.xlabel('Wavelength (microns)')\n", + "plt.ylabel('Flux')" + ] + }, + { + "cell_type": "markdown", + "id": "1c8bab70", + "metadata": {}, + "source": [ + "By eye, the larger-scale features in this plot between 9 and 11 microns look like the $\\rm NH_3$ bands expected from ground-based, mid-infrared spectra of Jupiter. Additionally, the shorter-scale features near 12 microns seem reminiscent of $\\rm C_2H_6$ features expected based on previous observations. In particular, compare these spectra to Figure 2 of [Encrenaz et al. 1978](https://adsabs.harvard.edu/pdf/1978A%26A....70...29E)." + ] + }, + { + "cell_type": "markdown", + "id": "c90e6091", + "metadata": {}, + "source": [ + "[Top of Page](#top)\n", + "\"Space\n" + ] + } + ], + "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": "3.10.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/MAST/exploring_jwst_transmission/requirements.txt b/notebooks/MAST/exploring_jwst_transmission/requirements.txt new file mode 100644 index 00000000..e4910676 --- /dev/null +++ b/notebooks/MAST/exploring_jwst_transmission/requirements.txt @@ -0,0 +1 @@ +jdaviz==2.6.0 \ No newline at end of file