SED fitting with MCMC: methodology and application to large galaxy surveys

TL;DR

This paper introduces two Markov Chain Monte Carlo tools, GalMC and SpeedyMC, for spectral energy distribution fitting in galaxy surveys, highlighting their efficiency and application to large datasets.

Contribution

The paper presents SpeedyMC, a faster SED fitting algorithm based on pre-computed templates, enabling rapid analysis of large galaxy survey data.

Findings

SpeedyMC reduces SED fitting time by a factor of 20,000.

GalMC provides detailed SED parameter inference with full flexibility.

Application to z~3 Lyman Alpha Emitting galaxies demonstrates the methods' effectiveness.

Abstract

We present GalMC (Acquaviva et al 2011), our publicly available Markov Chain Monte Carlo algorithm for SED fitting, show the results obtained for a stacked sample of Lyman Alpha Emitting galaxies at z ~ 3, and discuss the dependence of the inferred SED parameters on the assumptions made in modeling the stellar populations. We also introduce SpeedyMC, a version of GalMC based on interpolation of pre-computed template libraries. While the flexibility and number of SED fitting parameters is reduced with respect to GalMC, the average running time decreases by a factor of 20,000, enabling SED fitting of each galaxy in about one second on a 2.2GHz MacBook Pro laptop, and making SpeedyMC the ideal instrument to analyze data from large photometric galaxy surveys.