Removing biases in resolved stellar mass-maps of galaxy disks through successive Bayesian marginalization

TL;DR

This paper introduces Bayesian Successive Priors (BSP), an iterative Bayesian method that reduces biases in resolved stellar mass maps of galaxy disks, leading to more accurate mass distribution assessments.

Contribution

The paper presents a novel Bayesian marginalization algorithm, BSP, that mitigates spatial biases in stellar mass maps, improving the accuracy of galaxy mass distribution analysis.

Findings

BSP reduces the bias in mass maps compared to previous methods.

Mass missed by unresolved studies is about half of what was previously estimated.

BSP mass-maps better resemble near-infrared images.

Abstract

Stellar masses of galaxies are frequently obtained by fitting stellar population synthesis models to galaxy photometry or spectra. The state of the art method resolves spatial structures within a galaxy to assess the total stellar mass content. In comparison to unresolved studies, resolved methods yield, on average, higher fractions of stellar mass for galaxies. In this work we improve the current method in order to mitigate a bias related to the resolved spatial distribution derived for the mass. The bias consists in an apparent filamentary mass distribution, and a spatial coincidence between mass structures and dust lanes near spiral arms. The improved method is based on iterative Bayesian marginalization, through a new algorithm we have named Bayesian Successive Priors (BSP). We have applied BSP to M 51, and to a pilot sample of 90 spiral galaxies from the Ohio State University Bright Spiral Galaxy Survey. By comparing quantitatively both methods, we find that the average fraction of stellar mass missed by unresolved studies is only half than previously thought. In contrast with the previous method, the output BSP mass-maps bear a better resemblance to near infrared images.