We present Bayesian Analysis of Galaxies for Physical Inference andParameter EStimation, or BAGPIPES, a new PYTHON tool that can be used torapidly generate complex model galaxy spectra and to fit these toarbitrary combinations of spectroscopic and photometric data using theMULTINEST nested sampling algorithm. We extensively test our ability torecover realistic star formation histories (SFHs) by fitting mockobservations of quiescent galaxies from the MUFASA simulation. We thenperform a detailed analysis of the SFHs of a sample of 9289 quiescentgalaxies from UltraVISTA with stellar masses, M* >1010 M⊙ and redshifts 0.25 <z <3.75.The majority of our sample exhibit SFHs that rise gradually then quenchrelatively rapidly over 1-2 Gyr. This behaviour is consistent withrecent cosmological hydrodynamic simulations, where AGN-driven feedbackin the low-accretion (jet) mode is the dominant quenching mechanism. Atz > 1, we also find a class of objects with SFHs that rise and fallvery rapidly, with quenching time-scales of 3 Gyr, which wespeculate to be the result of diminishing overall cosmic gas supply. Weconfirm the mass-accelerated evolution (downsizing) trend, and a trendtowards more rapid quenching at higher stellar masses. However, ourresults suggest that the latter is a natural consequence ofmass-accelerated evolution, rather than a change in quenching physicswith stellar mass. We find 61 ± 8 per cent of z > 1.5massive-quenched galaxies undergo significant further evolution by z =0.5. BAGPIPES is available at bagpipes.readthedocs.io.
|Journal||Monthly Notices of the Royal Astronomical Society|
|Publication status||Published - 8 Aug 2018|
- methods: statistical
- galaxies: evolution
- galaxies: star formation
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- School of Physics and Astronomy - Chancellor's Fellow
Person: Academic: Research Active (Research Assistant)