iris is a Python package that simulates IR molecular line emission from protoplanetary disks.
Get the latest version from PyPi
pip install iris-jwst
jax, jaxlib, astroquery, astropy, pandas
The emission lines are modeled using isothermal slabs, with a detailed wavelength-dependent opacity treatment that accounts for overlapping lines and saturation effects.
iris is vectorized and optimized with jax, i.e. the code is dynamically compiled and can (but does not need to) be significantly sped-up using GPUs. e.g. we can generate a full JWST MIRI MRS model spectrum including multiple species in < 5 ms
One can use either a single slab, or a set of slabs to reproduce the effects of radial temperature and density gradients in the disk.
To model emission from CO and H2O using a single slab for each:
import numpy as np
import iris as iris
from iris import setup
'''First time running iris, we need to download the HITRAN line list'''
path_to_moldata = './your_path/HITRAN' # path where we want to save the HITRAN data
# Let's get the data for H2O and CO
setup.setup_linelists('H2O', 'H2O', 1, path_to_moldata)
setup.setup_linelists('CO', 'CO', 1, path_to_moldata)
# Define a fine wavelength grid (in micron) to evaluate opacities
fine_wgrid = np.arange(4.7,8.6,6e-5)
# Define a wavelength grid (in micron) to downsample the model
obs_wgrid = np.arange(4.8,8.5,0.002)
# The instrument resolving power
R = 3200
# DISK MODEL
'''Important note: You need to add the molecules in ALPHABETICAL ORDER'''
'''This is just because of how JAX compiles dictionaries.'''
'''So here e.g. we add CO before H2O'''
slab = iris.slab(molecules=['CO', 'H2O'], wlow=4.8, whigh=26.0, path_to_moldata=path_to_moldata)
# Distance to source
distance = 120 # pc
# NOTE that we define an ARRAY for the temperature, column density, and area FOR EACH SPECIES.
# So if we wanted to add multiple components for a species just add more points to each array.
# Excitation temperatures for each molecule in K
T_ex = np.array([np.array([600.0]), # for CO
np.array([800.0])]) # for H2O
# column densities in cm^-2
N_mol = np.array([np.array([1e17]), # for CO
np.array([1e16])]) # for H2O
# emitting areas in au^2
A_au = np.array([np.array([2.0]), # for CO
np.array([1.0])]) # for H2O
# turbulent line widths in km/s (line FWHM)
dV = np.array([np.array([2.0]), # for CO
np.array([2.0])]) # for H2O
# model spectrum
slab.setup_disk(distance, T_ex, N_mol, A_au, dV)
slab.setup_grid(fine_wgrid, obs_wgrid, R)
slab.simulate()
# Plot slab.downsampled_flux:
