TL;DR
This paper introduces BAGPIPES, a Python tool for modeling galaxy spectra, and uses it to analyze star-formation histories of quiescent galaxies, revealing multiple quenching mechanisms and evolutionary trends across redshifts.
Contribution
The paper presents BAGPIPES, a new Bayesian spectral fitting tool, and applies it to large galaxy samples to uncover diverse quenching processes and their relation to galaxy evolution.
Findings
Most galaxies quench rapidly after a gradual rise in star formation.
High-redshift galaxies show rapid quenching consistent with quasar-mode AGN feedback.
A significant fraction of high-redshift quenched galaxies evolve further by lower redshifts.
Abstract
We present Bayesian Analysis of Galaxies for Physical Inference and Parameter EStimation, or BAGPIPES, a new Python tool which can be used to rapidly generate complex model galaxy spectra and to fit these to arbitrary combinations of spectroscopic and photometric data using the MultiNest nested sampling algorithm. We extensively test our ability to recover realistic star-formation histories (SFHs) by fitting mock observations of quiescent galaxies from the MUFASA simulation. We then perform a detailed analysis of the SFHs of a sample of 9289 quiescent galaxies from UltraVISTA with stellar masses, and redshifts . The majority of our sample exhibit SFHs which rise gradually then quench relatively rapidly, over Gyr. This behaviour is consistent with recent cosmological hydrodynamic simulations, where AGN-driven feedback in the…
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