{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# High-Resolution Spectroscopy\n", "\n", "This tutorial demonstrates how to use POSEIDON's forward model, TRIDENT, to generate grids of high spectral resolution models. We'll assume familiarity with the main functions covered in [\\\"Generating Transmission Spectra\\\"](transmission_basic.html).\n", "\n", "As a case study, here we consider WASP-76b — a hot Jupiter with many recent chemical species detections at high-resolution. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we define the main star and planet properties for the WASP-76 system (the values below are from [exo.MAST](https://exo.mast.stsci.edu/))." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from POSEIDON.core import create_star, create_planet\n", "from POSEIDON.constants import R_Sun, R_J\n", "import numpy as np\n", "\n", "#***** Define stellar properties *****#\n", "\n", "R_s = 1.73*R_Sun # Stellar radius (m)\n", "T_s = 6250.0 # Stellar effective temperature (K)\n", "Met_s = 0.23 # Stellar metallicity [log10(Fe/H_star / Fe/H_solar)]\n", "log_g_s = 4.13 # Stellar log surface gravity (log10(cm/s^2) by convention)\n", "\n", "# Create the stellar object\n", "star = create_star(R_s, T_s, log_g_s, Met_s)\n", "\n", "#***** Define planet properties *****#\n", "\n", "planet_name = 'WASP-76b' # Planet name used for plots, output files etc.\n", "\n", "R_p = 1.82*R_J # Planetary radius (m)\n", "log_g_p = 2.8331 # Gravitational field of planet (cgs)\n", "T_eq = 2182.12 # Equilibrium temperature (K)\n", "\n", "# Create the planet object\n", "planet = create_planet(planet_name, R_p, log_g = log_g_p, T_eq = T_eq)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Creating a High-Resolution Transmission Spectrum" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's first generate a high-resolution template model for $\\rm{Fe}$. We'll assume a simple isothermal, cloud-free atmosphere with a solar $\\rm{Fe}$ abundance." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Free parameters: ['R_p_ref' 'T' 'log_Fe']\n" ] } ], "source": [ "from POSEIDON.core import define_model\n", "\n", "#***** Define model *****#\n", "\n", "model_name = 'Template_Fe' # Model name used for plots, output files etc.\n", "\n", "bulk_species = ['H2', 'He'] # H2 + He comprises the bulk atmosphere\n", "param_species = ['Fe'] # We only need one chemical species for this template\n", "\n", "# Create the model object\n", "model = define_model(model_name, bulk_species, param_species, \n", " PT_profile = 'isotherm', cloud_model = 'cloud-free')\n", "\n", "# Check the free parameters defining this model\n", "print(\"Free parameters: \" + str(model['param_names']))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from POSEIDON.core import make_atmosphere\n", "import numpy as np\n", "\n", "# Specify the pressure grid of the atmosphere\n", "P_min = 1.0e-10 # Extend down to 0.1 nbar to avoid strong line cores 'clipping' at high-res\n", "P_max = 100 # 100 bar\n", "N_layers = 100 # 100 layers\n", "\n", "# We'll space the layers uniformly in log-pressure\n", "P = np.logspace(np.log10(P_max), np.log10(P_min), N_layers)\n", "\n", "# Specify the reference pressure and radius\n", "P_ref = 10.0 # Reference pressure (bar)\n", "R_p_ref = R_p # Radius at reference pressure\n", "\n", "# Provide a specific set of model parameters for the atmosphere \n", "PT_params = np.array([T_eq]) # Let's just use the equilibrium temperature\n", "log_X_params = np.array([-4.5]) # -4.5 is roughly the solar Fe abundance (Asplund+2021)\n", "\n", "# Generate the atmosphere\n", "atmosphere = make_atmosphere(planet, model, P, P_ref, R_p_ref, \n", " PT_params, log_X_params)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Line-by-line Transmission Spectra\n", "\n", "We'll now initialise POSEIDON's opacity database at its native resolution ($\\Delta \\tilde{\\nu} = 0.01$ cm-1, equivalent to $R = \\lambda / \\Delta \\lambda = 10^6$ at 1 micron). Since this resolution is sufficient to resolve the line profiles of individual transition lines, such high spectral resolution models are commonly called 'line-by-line' models.\n", "\n", "For our $\\rm{Fe}$ template, let's generate a high-resolution model covering optical wavelengths." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from POSEIDON.core import wl_grid_line_by_line\n", "from POSEIDON.core import read_opacities\n", "\n", "#***** Wavelength grid *****#\n", " \n", "wl_min = 0.3 # Minimum wavelength (um)\n", "wl_max = 1.0 # Maximum wavelength (um) \n", "\n", "wl_high_res = wl_grid_line_by_line(wl_min, wl_max)\n", "\n", "#***** Read opacity data *****#\n", "\n", "opacity_treatment = 'line_by_line'\n", "\n", "# Prepare high-resolution opacity data\n", "opac_high_res = read_opacities(model, wl_high_res, opacity_treatment)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice how the opacity interpolation is almost instantaneous?\n", "\n", "Unlike the opacity sampling mode (covered in [\\\"Generating Transmission Spectra with TRIDENT\\\"](transmission_basic.html)), POSEIDON does not pre-interpolate cross sections in line-by-line mode (to conserve RAM). Instead, the cross sections are interpolated onto the specific layer pressure and temperatures of the atmosphere only when the user generates a spectrum. Hence, all of the computational burden occurs inside the ``compute_spectrum`` function.\n", "\n", "The cell below should take around a minute to run." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reading in cross sections in line-by-line mode...\n", "H2-H2 done\n", "H2-He done\n", "Fe done\n", "Finished producing extinction coefficients\n" ] } ], "source": [ "from POSEIDON.core import compute_spectrum\n", "\n", "# Generate the high-resolution spectrum\n", "spectrum_high_res = compute_spectrum(planet, star, model, atmosphere, \n", " opac_high_res, wl_high_res,\n", " spectrum_type = 'transmission', \n", " save_spectrum = True, # Saves to ./POSEIDON_output/WASP-76b/spectra/\n", " disable_continuum = True) # Turns off Rayleigh scattering and CIA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that we provided two new optional arguments to the ``compute_spectrum`` function:\n", "\n", "* ``save_spectrum`` outputs a .txt file containing the model spectrum to the directory ``./POSEIDON_output/𝗽𝗹𝗮𝗻𝗲𝘁_𝗻𝗮𝗺𝗲/spectra/``.\n", "* ``disable_continuum`` turns off continuum opacity sources (i.e. Rayleigh scattering and CIA), which can be helpful for producing high-resolution absorption templates.\n", "\n", "Finally, let's plot our $\\rm{Fe}$ template." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from POSEIDON.utility import plot_collection\n", "from POSEIDON.visuals import plot_spectra\n", "\n", "# Add both the high-res spectrum to a new plot collection\n", "spectra = plot_collection(spectrum_high_res, wl_high_res, collection = [])\n", "\n", "# Produce figure\n", "fig = plot_spectra(spectra, planet, model, bin_spectra = False, # Skip plotting a low resolution binned model \n", " y_min = 1.1e-2, y_max = 2.1e-2,\n", " spectra_labels = ['Fe absorption'], plt_label = 'Full Template')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also zoom in on a narrow wavelength range to see individual $\\rm{Fe}$ lines." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Produce figure\n", "fig = plot_spectra(spectra, planet, model, bin_spectra = False,\n", " wl_min = 0.50, wl_max = 0.51, # 0.50 to 0.51 um\n", " y_min = 1.1e-2, y_max = 1.8e-2,\n", " spectra_labels = ['Fe absorption'], plt_label = 'Zoomed Template')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Creating a Grid of High-Resolution Spectra\n", "\n", "For cross correlation analyses, one will generally desire a grid of template models spanning different chemical species and atmospheric properties.\n", "\n", "You can straightforwardly make a grid of high-resolution spectra by constructing a loop over atmospheric properties. The cell below provides an example to build a simple grid of model templates for $\\rm{Fe}$ and $\\rm{Ca}^{+}$ spanning 3 temperatures and 5 mixing ratios (for a total of 30 models).\n", "\n", "
\n", "\n", " **Note:**\n", "\n", " Running the cell below will take around 30 minutes, with the grid using 2 GB of disk storage.\n", "\n", "
" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model 1/30 done.\n", "Model 2/30 done.\n", "Model 3/30 done.\n", "Model 4/30 done.\n", "Model 5/30 done.\n", "Model 6/30 done.\n", "Model 7/30 done.\n", "Model 8/30 done.\n", "Model 9/30 done.\n", "Model 10/30 done.\n", "Model 11/30 done.\n", "Model 12/30 done.\n", "Model 13/30 done.\n", "Model 14/30 done.\n", "Model 15/30 done.\n", "Model 16/30 done.\n", "Model 17/30 done.\n", "Model 18/30 done.\n", "Model 19/30 done.\n", "Model 20/30 done.\n", "Model 21/30 done.\n", "Model 22/30 done.\n", "Model 23/30 done.\n", "Model 24/30 done.\n", "Model 25/30 done.\n", "Model 26/30 done.\n", "Model 27/30 done.\n", "Model 28/30 done.\n", "Model 29/30 done.\n", "Model 30/30 done.\n" ] } ], "source": [ "from POSEIDON.core import define_model\n", "from POSEIDON.core import make_atmosphere\n", "from POSEIDON.core import wl_grid_line_by_line\n", "from POSEIDON.core import read_opacities\n", "from POSEIDON.core import compute_spectrum\n", "import numpy as np\n", "\n", "# Specify arrays of parameters to loop over\n", "T_arr = np.array([2000, 2200, 2400])\n", "log_X_arr = np.array([-8, -7, -6, -5, -4])\n", "template_species = np.array(['Fe', 'Ca+'])\n", "\n", "# Define model for grid\n", "bulk_species = ['H2', 'He'] # H2 + He comprises the bulk atmosphere\n", "\n", "# Specify the pressure grid of the atmosphere\n", "P_min = 1.0e-10 # 0.1 nbar\n", "P_max = 100 # 100 bar\n", "N_layers = 100 # 100 layers\n", "\n", "# We'll space the layers uniformly in log-pressure\n", "P = np.logspace(np.log10(P_max), np.log10(P_min), N_layers)\n", "\n", "# Specify the reference pressure and radius\n", "P_ref = 10.0 # Reference pressure (bar)\n", "R_p_ref = R_p # Radius at reference pressure\n", "\n", "#***** Wavelength grid *****#\n", " \n", "wl_min = 0.3 # Minimum wavelength (um)\n", "wl_max = 1.0 # Maximum wavelength (um) \n", "\n", "wl_high_res = wl_grid_line_by_line(wl_min, wl_max)\n", "\n", "#***** Read opacity data *****#\n", "\n", "opacity_treatment = 'line_by_line'\n", "\n", "model_count = 0 # Counter variable so user can track model grid progress\n", "\n", "# Loop over chemical species, temperatures, and mixing ratios\n", "for q in range(len(template_species)):\n", " for i in range(len(T_arr)):\n", " for j in range(len(log_X_arr)):\n", "\n", " model_count += 1\n", "\n", " # Load chemical species and atmospheric properties for this template\n", " param_species = [template_species[q]]\n", " T_i = T_arr[i]\n", " log_X_j = log_X_arr[j]\n", "\n", " # Define model name for this parameter combination\n", " model_name = param_species[0] + '_' + str(log_X_j) + '_T_' + str(T_i) + 'K'\n", "\n", " # Create the model object\n", " model = define_model(model_name, bulk_species, param_species, \n", " PT_profile = 'isotherm', cloud_model = 'cloud-free')\n", "\n", " # Prepare high-resolution opacity data\n", " opac_high_res = read_opacities(model, wl_high_res, opacity_treatment)\n", "\n", " # Store parameters in input array\n", " PT_params = np.array([T_i])\n", " log_X_params = np.array([[log_X_j]])\n", "\n", " # Generate the atmosphere\n", " atmosphere = make_atmosphere(planet, model, P, P_ref, R_p_ref,\n", " PT_params, log_X_params)\n", "\n", " # Compute the spectrum\n", " spectrum_high_res = compute_spectrum(planet, star, model, atmosphere, \n", " opac_high_res, wl_high_res,\n", " spectrum_type = 'transmission', \n", " save_spectrum = True,\n", " disable_continuum = True,\n", " suppress_print = True) # Suppress 'cross section done' print statements\n", "\n", " print(\"Model \" + str(model_count) + \"/\" + \n", " str(len(T_arr)*len(log_X_arr)*len(template_species)) + \" done.\")" ] } ], "metadata": { "kernelspec": { "display_name": "POSEIDON_python_3.11", "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": "3.11.9" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }