BayeSED: A General Approach to Fitting the Spectral Energy Distribution of Galaxies

TL;DR

BayeSED is a Bayesian spectral energy distribution fitting tool for galaxies that accurately recovers physical parameters and allows model comparison, providing insights into galaxy evolution.

Contribution

The paper introduces a new version of BayeSED with systematic testing, Bayesian model comparison capabilities, and application to a large galaxy sample, demonstrating its reliability and power.

Findings

BayeSED accurately recovers galaxy parameters from mock data.

Bayesian model comparison favors the Bruzual & Charlot (2003) model over Maraston (2005).

Physical parameters are consistent with other methods, with more natural distributions.

Abstract

We present a newly developed version of BayeSED, a general Bayesian approach to the spectral energy distribution (SED) fitting of galaxies. The new BayeSED code has been systematically tested on a mock sample of galaxies. The comparison between estimated and inputted value of the parameters show that BayeSED can recover the physical parameters of galaxies reasonably well. We then applied BayeSED to interpret the SEDs of a large Ks-selected sample of galaxies in the COSMOS/UltraVISTA field with stellar population synthesis models. With the new BayeSED code, a Bayesian model comparison of stellar population synthesis models has been done for the first time. We found that the model by Bruzual & Charlot (2003), statistically speaking, has larger Bayesian evidence than the model by Maraston (2005) for the Ks-selected sample. Besides, while setting the stellar metallicity as a free parameter obviously increases the Bayesian evidence of both models, varying the IMF has a notable effect only on the Maraston (2005) model. Meanwhile, the physical parameters estimated with BayeSED are found to be generally consistent with those obtained with the popular grid-based FAST code, while the former exhibits more natural distributions. Based on the estimated physical parameters of galaxies in the sample, we qualitatively classified the galaxies in the sample into five populations that may represent galaxies at different evolution stages or in different environments. We conclude that BayeSED could be a reliable and powerful tool for investigating the formation and evolution of galaxies from the rich multi-wavelength observations currently available. A binary version of MPI parallelized BayeSED code is publicly available at https://bitbucket.org/hanyk/bayesed.