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SED_Model

Synthetic photometry and stellar parameter inference.

SED_Model is a Python package for computing observer-ready synthetic spectral energy distributions (SEDs) and broadband magnitudes from stellar atmosphere grids, and for recovering stellar parameters from observed photometry via Bayesian MCMC inference. It works directly downstream of SED_Tools and as a standalone drop-in for workflows that use MESA-style atmosphere grids and filter transmission curves.

Current version: 0.1.7 (PyPI)

Two paired models, one parameter language

  • Forward model — maps (Teff, logg, [M/H], R, d) to an interpolated SED, bolometric flux and magnitude, and synthetic magnitudes in any set of loaded filters.
  • Inverse model — maps observed magnitudes with uncertainties to a posterior distribution over (Teff, logg, [M/H]), and optionally over Av and distance, via emcee MCMC sampling.

Both directions operate through a shared FitParams object. The same parameter specification flows from the forward model to the likelihood evaluator inside the sampler and back out again.

SED_Tools  ->  build/download SED grids and filters
SED_Model  ->  generate synthetic magnitudes and infer stellar parameters

Features

  • Hermite and linear interpolation over 3D atmosphere grids (Teff, logg, [M/H]).
  • Photon-counting filter convolution in Vega, AB, and ST photometric systems.
  • Precomputed zero-points matching MESA colors/private/synthetic.f90 conventions.
  • Pure-NumPy interstellar dust extinction with six supported laws, including Gordon et al. (2023).
  • Bayesian stellar-parameter inference via emcee with fixed, bounded, and fully free parameter modes.
  • Structured result containers with summaries, persistence to CSV and NPZ, and interpolation diagnostics.
  • Fortran kernels for performance-critical paths, compiled via f2py/Meson.
  • Forward tests validated against MESA colors reference outputs; inverse tests verified by self-consistent synthetic recovery.

Where to go next

  • Installation — install from PyPI or source, and build the Fortran extension.
  • Quick Start — a forward and an inverse run in a few lines.
  • User Guide — grids, filters, both models, parameter modes, extinction, and I/O.
  • Demonstrations — full walkthroughs of every script in demos/, with the actual source embedded.
  • API Reference — generated directly from the docstrings in the source.

License

SED_Model uses a mixed-license structure. The Python source code, documentation, examples, tests, and packaging files are distributed under the MIT License. The Fortran numerical kernels in fortran/ are distributed under LGPL-3.0-or-later. See LICENSE and source file headers for details.