SED_Model is a Python package that offers advanced tools for generating synthetic spectral energy distributions (SEDs) and inferring stellar parameters from observed data using Bayesian methods. It integrates seamlessly with existing workflows and provides robust functionality for both forward and inverse modeling, making it vital for stellar astrophysics research.
SED_Model is an advanced Python package designed for synthetic photometry and stellar parameter inference, characterized by its capability to compute observer-ready synthetic spectral energy distributions (SEDs) as well as broadband magnitudes from stellar atmosphere grids. This repository offers functionality for recovering stellar parameters from observed photometry through a robust Bayesian MCMC inference approach.
(Teff), surface gravity (logg), metallicity [M/H], radius (R), and distance (d) into interpolated SEDs, bolometric flux, and magnitudes across various loaded filters.Av and distance, executed via emcee for MCMC sampling.f2py/Meson.from sed_model import load_grid, load_filters, run_forward
grid = load_grid("/path/to/Kurucz2003all/")
filters = load_filters(["/path/to/filters/GAIA/G.dat", "/path/to/filters/2MASS/J.dat",])
result = run_forward(teff=5777, logg=4.44, meta=0.0, R=6.957e10, d=3.086e19, grid=grid, filters=filters, mag_system="Vega")
print(result.magnitudes)
from sed_model import run_inverse
posterior = run_inverse(obs_magnitudes=[5.03, 4.17], obs_uncertainties=[0.01, 0.02], filter_names=["G", "J"], R=6.957e10, d=3.086e19, grid=grid, filters=filters, n_walkers=32, n_steps=1000, n_burn=300, seed=42)
posterior.print_summary()
This package is designed to function seamlessly with SED_Tools, which facilitates the acquisition and standardization of filter downloads, alongside grid construction and SED generation. By positioning SED_Model directly downstream, users can derive synthetic magnitudes and perform stellar parameter inference efficiently, enhancing the workflow:
SED_Tools -> build/download SED grids and filters
SED_Model -> generate synthetic magnitudes and infer stellar parameters
SED_Model stands as a comprehensive solution for astronomers and astrophysicists aiming to derive accurate stellar parameters and generate synthetic observational data, thus contributing significantly to the field of stellar astrophysics.
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