$\texttt{bayes_spec}$: A Bayesian Spectral Line Modeling Framework for Astrophysics

TL;DR

bayes_spec is a Bayesian framework for astrophysical spectral line modeling that allows flexible, user-defined models and uses advanced inference techniques to determine optimal cloud decomposition.

Contribution

It introduces a versatile Bayesian spectral line modeling framework supporting arbitrary models and cloud decomposition, with algorithms for optimal cloud number determination.

Findings

Supports complex physical models including radiative transfer

Uses MCMC methods for parameter inference

Provides algorithms for optimal cloud number selection

Abstract

$\texttt{bayes_spec}$ is a Bayesian spectral line modeling framework for astrophysics. Given a user-defined model and a spectral line dataset, $\texttt{bayes_spec}$ enables inference of the model parameters through different numerical techniques, such as Monte Carlo Markov Chain (MCMC) methods, implemented in the PyMC probabilistic programming library. The API for $\texttt{bayes_spec}$ is designed to support astrophysical researchers who wish to ``fit'' arbitrary, user-defined models, such as simple spectral line profile models or complicated physical models that include a full physical treatment of radiative transfer. These models are ``cloud-based'', meaning that the spectral line data are decomposed into a series of discrete clouds with parameters defined by the user's model. Importantly, $\texttt{bayes_spec}$ provides algorithms to determine the optimal number of clouds for a given model and dataset.