CIGALEMC: Galaxy Parameter Estimation using a Markov Chain Monte Carlo Approach with Cigale

TL;DR

This paper presents CIGALEMC, an MCMC-based enhancement of the CIGALE code, enabling faster and more accurate galaxy parameter estimation from spectral energy distributions compared to traditional grid-based methods.

Contribution

The paper introduces CIGALEMC, a novel MCMC implementation for galaxy parameter estimation that improves efficiency and accuracy over existing grid-based approaches.

Findings

CIGALEMC accurately retrieves input parameters from simulated data.

The MCMC approach reduces computation time significantly.

Application to real data demonstrates reliable parameter constraints.

Abstract

We introduce a fast Markov Chain Monte Carlo (MCMC) exploration of the astrophysical parameter space using a modified version of the publicly available code CIGALE (Code Investigating GALaxy emission). The original CIGALE builds a grid of theoretical Spectral Energy Distribution (SED) models and fits to photometric fluxes from Ultraviolet (UV) to Infrared (IR) to put contraints on parameters related to both formation and evolution of galaxies. Such a grid-based method can lead to a long and challenging parameter extraction since the computation time increases exponentially with the number of parameters considered and results can be dependent on the density of sampling points, which must be chosen in advance for each parameter. Markov Chain Monte Carlo methods, on the other hand, scale approximately linearly with the number of parameters, allowing a faster and more accurate exploration of the parameter space by using a smaller number of efficiently chosen samples. We test our MCMC version of the code CIGALE (called CIGALEMC) with simulated data. After checking the ability of the code to retrieve the input parameters used to build the mock sample, we fit theoretical SEDs to real data from the well known and studied SINGS sample. We discuss constraints on the parameters and show the advantages of our MCMC sampling method in terms of accuracy of the results and optimization of CPU time.