Hierarchical Bayesian approach for estimating physical properties in spiral galaxies: Age Maps for M74

TL;DR

This paper introduces a hierarchical Bayesian model to estimate the ages of star-forming regions in galaxy M74 using multi-band imaging data, improving the accuracy of physical parameter inference in astronomy.

Contribution

It presents a novel hierarchical Bayesian approach that accounts for uncertainties and parameter interrelationships in estimating stellar population ages from flux ratios.

Findings

Successful age mapping of M74's star-forming regions.

Demonstrated the effectiveness of Bayesian hierarchical modeling in astrophysical parameter estimation.

Provided a framework for integrating multi-band observational data with theoretical models.

Abstract

One of the fundamental goals of modern Astronomy is to estimate the physical parameters of galaxies from images in different spectral bands. We present a hierarchical Bayesian model for obtaining age maps from images in the \Ha\ line (taken with Taurus Tunable Filter (TTF)), ultraviolet band (far UV or FUV, from GALEX) and infrared bands (24, 70 and 160 microns ($\mu$m), from Spitzer). As shown in S\'anchez-Gil et al. (2011), we present the burst ages for young stellar populations in the nearby and nearly face on galaxy M74. As it is shown in the previous work, the \Ha\ to FUV flux ratio gives a good relative indicator of very recent star formation history (SFH). As a nascent star-forming region evolves, the \Ha\ line emission declines earlier than the UV continuum, leading to a decrease in the \Ha\/FUV ratio. Through a specific star-forming galaxy model (Starburst 99, SB99), we can obtain the corresponding theoretical ratio \Ha\ / FUV to compare with our observed flux ratios, and thus to estimate the ages of the observed regions. Due to the nature of the problem, it is necessary to propose a model of high complexity to take into account the mean uncertainties, and the interrelationship between parameters when the \Ha\ / FUV flux ratio mentioned above is obtained. To address the complexity of the model, we propose a Bayesian hierarchical model, where a joint probability distribution is defined to determine the parameters (age, metallicity, IMF), from the observed data, in this case the observed flux ratios \Ha\ / FUV. The joint distribution of the parameters is described through an i.i.d. (independent and identically distributed random variables), generated through MCMC (Markov Chain Monte Carlo) techniques.