Photometric stellar masses for galaxies in DESI Legacy Imaging Surveys

TL;DR

This work presents a simple yet robust photometric estimator for galaxy stellar masses using DESI Legacy Imaging Surveys, calibrated against precise S$^4$G masses. The authors show that a two-band formula, $\log(M_*/M_{\odot}) = 0.673\,M_g - 1.108\,M_r + 0.996$, achieves about 25% scatter, with little gain from including the $z$ band or additional structural parameters. They provide a Python tool, photomass_ls.py, to automate the workflow from image download to mass estimation, and explore alternative calibrations based on Meidt 2014, Querejeta 2015, and SGA-2020 magnitudes. Rest-frame corrections and cross-survey comparisons (e.g., GAMA) reveal systematic uncertainties primarily tied to reference calibrations and data depth. The method offers a scalable path to mass estimates for large galaxy samples, with clearly documented limitations and public data access.

Abstract

In many areas of extragalactic astrophysics, we need to convert the luminosity of a galaxy into its stellar mass. In this work, we aim to find a simple and effective formula to estimate the stellar mass from the images of galaxies delivered by the currently popular DESI Legacy Imaging Surveys. This survey provides an unsurpassed combination of a deep imaging with an extensive sky coverage in up to four photometric bands. We calibrated the sought formula against a sample of local galaxies observed by the Spitzer Survey of Stellar Structure in Galaxies (S$^4$G) that was directly dedicated to measure the stellar masses. For the absolute magnitudes $M_g$ and $M_r$ of a galaxy in the Legacy Surveys $g$ and $r$ bands, we find that the stellar masses can be estimated as $0.673M_g - 1.108M_r + 0.996$ with the scatter of 25\%. Employing more complex functions does not improve the estimate appreciably, even after including the galaxy ellipticity, Sérsic index, or the magnitudes in different Legacy Surveys bands. Generally, measurements in $r$ band were the most helpful ones, while adding $z$-band measurements did not improve the mass estimate much. We provide a Python-based script \texttt{photomass\_ls.py} to automatically download images of any galaxy from the Legacy Surveys database, create image masks, generate GALFIT input files with well-assessed initial values, perform the GALFIT photometry, and calculate the stellar mass estimate. Additionally, we tuned another version of the formula to the magnitudes provided by the Siena Galaxy Atlas 2020 (SGA-2020) with a scatter of 29\%. For both\,--\,our default and SGA-2020 formula, we offer two alternatives derived from different calibrations of S$^4$G masses that were based on different methods and assumptions.

Figures (7)

• Figure 1: Correlation of stellar masses drawn from S$^4$G with those computed by Eq. (\ref{['eq:f']}) using magnitudes measured in the Legacy Surveys data. The galaxies excluded from the fit, see Sect. \ref{['ssec:out']}, are highlighted by grey circles.
• Figure 2: The 15 most deviant galaxies relative to the relation described by Eq. (\ref{['eq:f']}).
• Figure 3: The 15 galaxies that are most closely aligned with the relation Eq. (\ref{['eq:f']}).
• Figure 4: Histogram of differences between S$^4$G stellar masses and stellar masses recovered using $a M_g + b M_r + c$ formulas: blue histograms -- GALFIT magnitudes with parameters listed in Tab. \ref{['tab:fits1']}, i.e. Eq. (\ref{['eq:f']}); the top red histogram -- using SGA-2020 $M_{26}$, i.e. Eq. (\ref{['eq:sga']}); and the bottom red histogram -- using SGA-2020 $M_\text{TOT}$, parameter values of the formula used with SGA-2020 magnitudes are listed in Tab. \ref{['tab:sgafits']}.
• Figure 5: Histogram of differences between S$^4$G stellar masses and stellar masses computed by Eq. (\ref{['eq:f']}) for the blue histogram and via mass-to-light ratios using tables in bell03 for the red histogram.