bayes_spec: A Bayesian Spectral Line Modeling Framework for Astrophysics

Abstract

The study of the interstellar medium (ISM)—the matter between the stars—relies heavily on the tools of spectroscopy. Spectral line observations of atoms, ions, and molecules in the ISM reveal the physical conditions and kinematics of the emitting gas. Robust and efficient numerical techniques are thus necessary for inferring the physical conditions of the ISM from observed spectral line data. bayes_spec is a Bayesian spectral line modeling framework for astrophysics. Given a userdefined model and a spectral line dataset, 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 (Oriol et al., 2023). The API for 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, bayes_spec provides algorithms to determine the optimal number of clouds for a given model and dataset.