On the Uncertainties of Stellar Mass Estimates via Colour Measurements

TL;DR

This study evaluates the uncertainties and biases in stellar mass estimates derived from colour measurements using MLCRs, comparing them with SED fitting, and provides guidelines for their application based on data complexity and size.

Contribution

It systematically quantifies uncertainties in MLCR-based stellar mass estimates, compares them with SED fitting, and offers practical recommendations for their use in various observational scenarios.

Findings

Errors decrease with more photometric bands used.

Prior assumptions dominate the error budget, causing colour-dependent biases.

MLCRs and SED fitting yield similar random errors (~0.1-0.14 dex).

Abstract

Mass-to-light versus colour relations (MLCRs), derived from stellar population synthesis models, are widely used to estimate galaxy stellar masses (M$_*$) yet a detailed investigation of their inherent biases and limitations is still lacking. We quantify several potential sources of uncertainty, using optical and near-infrared (NIR) photometry for a representative sample of nearby galaxies from the Virgo cluster. Our method for combining multi-band photometry with MLCRs yields robust stellar masses, while errors in M$_*$ decrease as more bands are simultaneously considered. The prior assumptions in one's stellar population modelling dominate the error budget, creating a colour-dependent bias of up to 0.6 dex if NIR fluxes are used (0.3 dex otherwise). This matches the systematic errors associated with the method of spectral energy distribution (SED) fitting, indicating that MLCRs do not suffer from much additional bias. Moreover, MLCRs and SED fitting yield similar degrees of random error ($\sim$0.1-0.14 dex) when applied to mock galaxies and, on average, equivalent masses for real galaxies with M$_* \sim$ 10$^{8-11}$ M$_{\odot}$. The use of integrated photometry introduces additional uncertainty in M$_*$ measurements, at the level of 0.05-0.07 dex. We argue that using MLCRs, instead of time-consuming SED fits, is justified in cases with complex model parameter spaces (involving, for instance, multi-parameter star formation histories) and/or for large datasets. Spatially-resolved methods for measuring M$_*$ should be applied for small sample sizes and/or when accuracies less than 0.1 dex are required. An Appendix provides our MLCR transformations for ten colour permutations of the $grizH$ filter set.