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.

Features

• Forward Model: Translates intrinsic stellar parameters such as effective temperature (Teff), surface gravity (logg), metallicity [M/H], radius (R), and distance (d) into interpolated SEDs, bolometric flux, and magnitudes across various loaded filters.
• Inverse Model: Converts observed magnitudes—coupled with their uncertainties—into a posterior distribution over stellar parameters, enabling optional recovery of foreground extinction Av and distance, executed via emcee for MCMC sampling.

Key Functionalities

• Efficient interpolation of 3D atmosphere grids using either Hermite or linear methods.
• Integration of photon-counting filter convolution compatible with Vega, AB, and ST photometric systems.
• Application of precomputed zero-points aligning with MESA conventions.
• Supports various interstellar dust extinction laws implemented in pure-NumPy.
• Provision of structured result containers, allowing data persistence through CSV and NPZ formats, and includes diagnostics for interpolation.
• Performance-critical paths optimized with Fortran kernels, compiled using f2py/Meson.

Practical Application

Forward Modeling Example

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)

Inverse Modeling Example

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()

Integration with SED_Tools

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.