{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Coronagraph Wedge Masks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The notebook builds on the concepts introduced in `Coronagraph_Basics.ipynb`. Specifically, we concentrate on the complexities involved in simulating the wedge coronagraphs." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Import the usual libraries\n", "import numpy as np\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "\n", "# Enable inline plotting at lower left\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will start by first importing `pynrc` along with the `obs_hci` (High Contrast Imaging) class, which lives in the `pynrc.obs_nircam` module. " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pyNRC log messages of level WARN and above will be shown.\n", "pyNRC log outputs will be directed to the screen.\n" ] } ], "source": [ "import pynrc\n", "from pynrc import nrc_utils # Variety of useful functions and classes\n", "from pynrc.obs_nircam import obs_hci # High-contrast imaging observation class\n", "\n", "# Progress bar\n", "from tqdm.auto import tqdm, trange\n", "\n", "# Disable informational messages and only include warnings and higher\n", "pynrc.setup_logging(level='WARN')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Source Definitions\n", "\n", "In the previous notebook, we simply used the `stellar_spectrum` functions to create sources normalized at their observed K-Band magnitude. This time, we will utilize the `source_spectrum` class to generate a model fit to the known spectrophotometry. The user can find the relevant photometric data at http://vizier.u-strasbg.fr/vizier/sed/ and click download data as a VOTable." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Define 2MASS Ks bandpass and source information\n", "bp_k = pynrc.bp_2mass('k')\n", "\n", "# Science source, dist, age, sptype, Teff, [Fe/H], log_g, mag, band\n", "args_sources = [('HR 8799', 39.0, 30, 'F0V', 7430, -0.47, 4.35, 5.24, bp_k)]\n", "\n", "# References source, sptype, Teff, [Fe/H], log_g, mag, band\n", "ref_sources = [('HD 220657', 'F8III', 5888, -0.01, 3.22, 3.04, bp_k)]\n", "\n", "# Directory housing VOTables \n", "# http://vizier.u-strasbg.fr/vizier/sed/\n", "votdir = 'votables/'" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.98590364]\n" ] } ], "source": [ "# Fit spectrum to SED photometry\n", "i=0\n", "name_sci, dist_sci, age, spt_sci, Teff_sci, feh_sci, logg_sci, mag_sci, bp_sci = args_sources[i]\n", "vot = votdir + name_sci.replace(' ' ,'') + '.vot'\n", "\n", "args = (name_sci, spt_sci, mag_sci, bp_sci, vot)\n", "kwargs = {'Teff':Teff_sci, 'metallicity':feh_sci, 'log_g':logg_sci}\n", "src = nrc_utils.source_spectrum(*args, **kwargs)\n", "\n", "src.fit_SED(use_err=False, robust=False, wlim=[1,5])\n", "\n", "# Final source spectrum\n", "sp_sci = src.sp_model" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1.07580856]\n" ] } ], "source": [ "# Do the same for the reference source\n", "name_ref, spt_ref, Teff_ref, feh_ref, logg_ref, mag_ref, bp_ref = ref_sources[i]\n", "vot = votdir + name_ref.replace(' ' ,'') + '.vot'\n", "\n", "args = (name_ref, spt_ref, mag_ref, bp_ref, vot)\n", "kwargs = {'Teff':Teff_ref, 'metallicity':feh_ref, 'log_g':logg_ref}\n", "ref = nrc_utils.source_spectrum(*args, **kwargs)\n", "\n", "ref.fit_SED(use_err=False, robust=False, wlim=[0.5,10])\n", "\n", "# Final reference spectrum\n", "sp_ref = ref.sp_model" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot spectra \n", "fig, axes = plt.subplots(1,2, figsize=(13,4.5))\n", "src.plot_SED(xr=[0.3,10], ax=axes[0])\n", "ref.plot_SED(xr=[0.3,10], ax=axes[1])\n", "\n", "axes[0].set_title('Science Specta -- {} ({})'.format(src.name, spt_sci))\n", "axes[1].set_title('Refrence Specta -- {} ({})'.format(ref.name, spt_ref))\n", "\n", "fig.tight_layout()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot the two spectra\n", "fig, ax = plt.subplots(1,1, figsize=(8,5))\n", "\n", "xr = [2.5,5.5]\n", "\n", "for sp in [sp_sci, sp_ref]:\n", " w = sp.wave / 1e4\n", " ind = (w>=xr[0]) & (w<=xr[1])\n", " sp.convert('Jy')\n", " f = sp.flux / np.interp(4.0, w, sp.flux)\n", " ax.semilogy(w[ind], f[ind], lw=1.5, label=sp.name)\n", " ax.set_ylabel('Flux (Jy) normalized at 4 $\\mu m$')\n", " sp.convert('flam')\n", "\n", "ax.set_xlim(xr)\n", "ax.set_xlabel(r'Wavelength ($\\mu m$)')\n", "ax.set_title('Spectral Sources')\n", "\n", "# Overplot Filter Bandpass\n", "bp = pynrc.read_filter('F460M', 'WEDGELYOT', 'MASKLWB')\n", "ax2 = ax.twinx()\n", "ax2.plot(bp.wave/1e4, bp.throughput, color='C2', label=bp.name+' Bandpass')\n", "ax2.set_ylim([0,0.8])\n", "ax2.set_xlim(xr)\n", "ax2.set_ylabel('Bandpass Throughput')\n", "\n", "ax.legend(loc='upper left')\n", "ax2.legend(loc='upper right')\n", "\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Initialize Observation\n", "\n", "Now we will initialize the high-contrast imaging class `pynrc.obs_hci` using the spectral objects and various other settings. The `obs_hci` object is a subclass of the more generalized `NIRCam` class. It implements new settings and functions specific to high-contrast imaging observations for corongraphy and direct imaging.\n", "\n", "For this tutorial, we want to observe these targets using the `MASKLWB` coronagraph in the `F460M` filter. All wedge coronagraphic masks such as the `MASKLWB` (B=bar) should be paired with the `WEDGELYOT` pupil element. Observations in the LW channel are most commonly observed in `WINDOW` mode with a 320x320 detector subarray size. Full detector sizes are also available.\n", "\n", "The wedge coronagraphs have an additional option to specify the location along the wedge to place your point source via the `bar_offset` keyword. If not specified, the location is automatically chosen based on the filter. A positive value will move the source to the right when viewing in 'sci' coordinate convention. Specifying this location is a non-standard mode.\n", "\n", "In this case, we're going to place our PSF at the narrow end of the LW bar, located at `bar_offset=8` arcsec from the bar center." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "filt, mask, pupil = ('F460M', 'MASKLWB', 'WEDGELYOT')\n", "wind_mode, subsize = ('WINDOW', 320)\n", "fov_pix, oversample = (321, 2)\n", "\n", "obs = pynrc.obs_hci(sp_sci, dist_sci, sp_ref=sp_ref, bar_offset=8, use_ap_info=False,\n", " filter=filt, image_mask=mask, pupil_mask=pupil,\n", " wind_mode=wind_mode, xpix=subsize, ypix=subsize, \n", " fov_pix=fov_pix, oversample=oversample, large_grid=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Just as a reminder, information for the reference observation is stored in the attribute `obs.Detector_ref`, which is simply it's own isolated `DetectorOps` class. The `bar_offset` value is initialized to be the same as the science observation. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exposure Settings\n", "\n", "Optimization of exposure settings are demonstrated in another tutorial, so we will not repeat that process here. We can assume that process was performed elsewhere to choose the `BRIGHT2` pattern with 10 groups and 40 total integrations. These settings apply to each roll position of the science observation as well as the for the reference observation." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "New Ramp Settings\n", " read_mode : BRIGHT2\n", " nf : 2\n", " nd2 : 0\n", " ngroup : 10\n", " nint : 40\n", "New Detector Settings\n", " wind_mode : WINDOW\n", " xpix : 320\n", " ypix : 320\n", " x0 : 275\n", " y0 : 1522\n", "New Ramp Times\n", " t_group : 2.138\n", " t_frame : 1.069\n", " t_int : 21.381\n", " t_int_tot1 : 22.470\n", " t_int_tot2 : 22.470\n", " t_exp : 855.232\n", " t_acq : 898.790\n" ] } ], "source": [ "# Update both the science and reference observations\n", "# These numbers come from GTO Proposal 1194\n", "obs.update_detectors(read_mode='BRIGHT2', ngroup=10, nint=40, verbose=True)\n", "obs.gen_ref_det(read_mode='BRIGHT2', ngroup=4, nint=90)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Add Planets\n", "\n", "There are four known giant planets orbiting HR 8799. Ideally, we would like to position them at their predicted locations on the anticipated observation date. For this case, we choose a plausible observation date of November 1, 2022. To convert between $(x,y)$ and $(r,\\theta)$, use the `nrc_utils.xy_to_rtheta` and `nrc_utils.rtheta_to_xy` functions.\n", "\n", "When adding the planets, it doesn't matter too much which exoplanet model spectrum we decide to use since the spectra are still fairly unconstrained at these wavelengths. We do know roughly the planets' luminosities, so we can simply choose some reasonable model and renormalize it to the appropriate filter brightness. \n", "\n", "Their are a few exoplanet models available to `pynrc` (SB12, BEX, COND). Let's choose those from Spiegel & Burrows (2012)." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# Projected locations for date 11/01/2022\n", "# These are prelimary positions, but within constrained orbital parameters\n", "loc_list = [(-1.625, 0.564), (0.319, 0.886), (0.588, -0.384), (0.249, 0.294)]\n", "\n", "# Estimated magnitudes within F444W filter\n", "pmags = [16.0, 15.0, 14.6, 14.7]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# Add planet information to observation class.\n", "# These are stored in obs.planets.\n", "# Can be cleared using obs.delete_planets().\n", "obs.delete_planets()\n", "for i, loc in enumerate(loc_list):\n", " obs.add_planet(model='SB12', mass=10, entropy=13, age=age, xy=loc, runits='arcsec', \n", " renorm_args=(pmags[i], 'vegamag', obs.bandpass))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Companions: 0%| | 0/4 [00:00" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from matplotlib.patches import Circle\n", "from pynrc.nrc_utils import plotAxes\n", "from pynrc.obs_nircam import get_cen_offsets\n", "\n", "fig, ax = plt.subplots(figsize=(6,6))\n", "\n", "xasec = obs.det_info['xpix'] * obs.pixelscale\n", "yasec = obs.det_info['ypix'] * obs.pixelscale\n", "extent = [-xasec/2, xasec/2, -yasec/2, yasec/2]\n", "xylim = 3\n", "\n", "vmin = 0\n", "vmax = 0.5*im_planets.max()\n", "ax.imshow(im_planets, extent=extent, vmin=vmin, vmax=vmax)\n", "\n", "# Overlay the coronagraphic mask\n", "detid = obs.Detector.detid\n", "im_mask = obs.mask_images['DETSAMP']\n", "# Do some masked transparency overlays\n", "masked = np.ma.masked_where(im_mask>0.95*im_mask.max(), im_mask)\n", "ax.imshow(1-masked, extent=extent, alpha=0.3, cmap='Greys_r', vmin=-0.5)\n", "\n", "\n", "for loc in loc_list:\n", " xc, yc = get_cen_offsets(obs, idl_offset=loc, PA_offset=PA_offset)\n", " circle = Circle((xc,yc), radius=xylim/15., alpha=0.7, lw=1, edgecolor='red', facecolor='none')\n", " ax.add_artist(circle)\n", "\n", "xlim = ylim = np.array([-1,1])*xylim\n", "xlim = xlim + obs.bar_offset\n", "ax.set_xlim(xlim)\n", "ax.set_ylim(ylim)\n", "\n", "ax.set_xlabel('Arcsec')\n", "ax.set_ylabel('Arcsec')\n", "\n", "ax.set_title('{} planets -- {} {}'.format(sp_sci.name, obs.filter, obs.image_mask))\n", "\n", "color = 'grey'\n", "ax.tick_params(axis='both', color=color, which='both')\n", "for k in ax.spines.keys():\n", " ax.spines[k].set_color(color)\n", "\n", "plotAxes(ax, width=1, headwidth=5, alength=0.15, angle=PA_offset, \n", " position=(0.1,0.1), label1='E', label2='N')\n", " \n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see, even with \"perfect PSF subtraction\" and no noise, it's difficult to make out planet e. This is primarily due to its location relative to the occulting mask reducing throughput combined with confusion of bright diffraction spots from nearby sources." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimated Performance\n", "\n", "Now we are ready to determine contrast performance and sensitivites as a function of distance from the star. \n", "\n", "### Roll-Subtracted Images\n", "\n", "First, we will create a quick simulated roll-subtracted image using the in `gen_roll_image` method. For the selected observation date of 11/1/2019, APT shows a PA range of 84$^{\\circ}$ to 96$^{\\circ}$. So, we'll assume Roll 1 has PA1=85, while Roll 2 has PA2=95. In this case, \"roll subtraction\" simply creates two science observations at two different parallactic angles and subtracts the same reference observation from each. The two results are then de-rotated to a common PA=0 and averaged.\n", "\n", "There is also the option to create ADI images, where the other roll position becomes the reference star by setting `no_ref=True`. \n", "\n", "### Contrast Curves\n", "\n", "Next, we will cycle through a few WFE drift values to get an idea of potential predicted sensitivity curves. The `calc_contrast` method returns a tuple of three arrays:\n", "1. The radius in arcsec.\n", "2. The n-sigma contrast.\n", "3. The n-sigma magnitude sensitivity limit (vega mag)." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "59dc9c83dee5417abd1882d903b47713", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/3 [00:00" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from pynrc.nb_funcs import plot_hdulist\n", "from matplotlib.patches import Circle\n", "\n", "fig, axes = plt.subplots(1,3, figsize=(14,4.3))\n", "xylim = 2.5\n", "xlim = ylim = np.array([-1,1])*xylim\n", "\n", "for j, wfe_drift in enumerate(wfe_list):\n", " ax = axes[j]\n", " hdul = hdul_dict[wfe_drift]\n", " \n", " plot_hdulist(hdul, xr=xlim, yr=ylim, ax=ax, vmin=0, vmax=2)\n", "\n", " # Location of planet\n", " for loc in loc_list:\n", " circle = Circle(loc, radius=xylim/15., lw=1, edgecolor='red', facecolor='none')\n", " ax.add_artist(circle)\n", "\n", " ax.set_title('$\\Delta$WFE = {:.0f} nm'.format(wfe_drift))\n", " \n", " nrc_utils.plotAxes(ax, width=1, headwidth=5, alength=0.15, position=(0.9,0.1), label1='E', label2='N')\n", "\n", "fig.suptitle('{} -- {} {}'.format(name_sci, obs.filter, obs.image_mask), fontsize=14)\n", "fig.tight_layout()\n", "fig.subplots_adjust(top=0.85)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e7f1728de4dc460896fd6680bbaad14c", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/3 [00:00" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from pynrc.nb_funcs import plot_contrasts, plot_planet_patches, plot_contrasts_mjup, update_yscale\n", "import matplotlib.patches as mpatches\n", "\n", "# fig, ax = plt.subplots(figsize=(8,5))\n", "fig, axes = plt.subplots(1,2, figsize=(14,4.5))\n", "xr=[0,5]\n", "yr=[24,8]\n", "\n", "# 1a. Plot contrast curves and set x/y limits\n", "ax = axes[0]\n", "ax, ax2, ax3 = plot_contrasts(curves, nsig, wfe_list, obs=obs, \n", " xr=xr, yr=yr, ax=ax, return_axes=True)\n", "# 1b. Plot the locations of exoplanet companions\n", "label = 'Companions ({})'.format(filt)\n", "planet_dist = [np.sqrt(x**2+y**2) for x,y in loc_list]\n", "ax.plot(planet_dist, pmags, marker='o', ls='None', label=label, color='k', zorder=10) \n", "\n", "# 1c. Plot Spiegel & Burrows (2012) exoplanet fluxes (Hot Start)\n", "plot_planet_patches(ax, obs, age=age, entropy=13, av_vals=None)\n", "ax.legend(ncol=2)\n", "\n", "# 2. Plot in terms of MJup using COND models\n", "ax = axes[1]\n", "ax1, ax2, ax3 = plot_contrasts_mjup(curves, nsig, wfe_list, obs=obs, age=age,\n", " ax=ax, twin_ax=True, xr=xr, yr=None, return_axes=True)\n", "yr = [0.03,100]\n", "for xval in planet_dist:\n", " ax.plot([xval,xval],yr, lw=1, ls='--', color='k', alpha=0.7)\n", "update_yscale(ax1, 'log', ylim=yr)\n", "yr_temp = np.array(ax1.get_ylim()) * 318.0\n", "update_yscale(ax2, 'log', ylim=yr_temp)\n", "ax.legend(loc='upper right', title='BEX ({:.0f} Myr)'.format(age))\n", "\n", "fig.suptitle('{} ({} + {})'.format(name_sci, obs.filter, obs.image_mask), fontsize=16)\n", "\n", "fig.tight_layout()\n", "fig.subplots_adjust(top=0.85, bottom=0.1 , left=0.05, right=0.97)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The innermost Planet e is above the detection threshold as suggested by the simulated images." ] }, { "cell_type": "code", "execution_count": null, "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": "3.7.9" } }, "nbformat": 4, "nbformat_minor": 2 }