Bayesian hierarchical modelling of the $\mathrm{M_{\star}}$-SFR relation from 1<z<6 in ASTRODEEP
L. Sandles, E. Curtis-Lake, S. Charlot, J. Chevallard, R. Maiolino

TL;DR
This paper employs Bayesian hierarchical modeling to analyze the evolution of the star-forming main sequence from redshift 1.25 to 6, revealing how galaxy star formation correlates with stellar mass and redshift, and highlighting current limitations in SED fitting.
Contribution
It introduces a Bayesian hierarchical approach to model the M_star-SFR relation across high redshifts, explicitly accounting for outliers and redshift evolution, with implications for future JWST data.
Findings
Main sequence normalization increases with redshift following gas accretion rates.
Slope of the M_star-SFR relation is approximately 0.79.
Current SED data are insufficient to fully constrain galaxy properties at high redshift.
Abstract
The Hubble Frontier Fields represent the opportunity to probe the high-redshift evolution of the main sequence of star-forming galaxies to lower masses than possible in blank fields thanks to foreground lensing of massive galaxy clusters. We use the BEAGLE SED-fitting code to derive stellar masses, , SFRs, and redshifts from galaxies within the ASTRODEEP catalogue. We fit a fully Bayesian hierarchical model of the main sequence over of the form while explicitly modelling the outlier distribution. The redshift-dependent intercept at is parametrized as . Our results agree with an increase in normalization of…
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Taxonomy
TopicsAstronomy and Astrophysical Research · Galaxies: Formation, Evolution, Phenomena · Gamma-ray bursts and supernovae
