Inferring the star formation histories of massive quiescent galaxies with bagpipes: evidence for multiple quenching mechanisms

By A. Carnall, R. McLure, J. Dunlop, R. Dav'e. Monthly notices of the Royal Astronomical Society, 2017

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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 algorithm. We extensively test our ability to recover realistic star-formation histories (SFHs) with BAGPIPES by fitting mock observations of quiescent galaxies from the MUFASA simulation. We show that a double-power-law model produces better agreement with realistic SFHs than an exponentially-declining model. We then perform a detailed analysis of the SFHs of a sample of 9312 quiescent galaxies from UltraVISTA with stellar masses, $M_* > 10^{10}\ \mathrm{M_\odot}$ and redshifts $0.25 < z_\mathrm{obs} < 3.75$. The majority of our quiescent sample exhibit SFHs which rise gradually then quench relatively rapidly, over $\sim 1{-}2$ Gyr. This behaviour is consistent with recent cosmological hydrodynamic simulations, where AGN-driven feedback in the low-accretion (jet) mode is the dominant quenching mechanism. In addition, we identify two further subsets of objects with distinct SFH shapes. At $z_\mathrm{obs} \gtrsim 1$ we find a class of objects with SFHs which rise and fall very rapidly, with quenching timescales of $\lesssim 1$ Gyr. These objects are consistent with (potentially merger-triggered) quasar-mode AGN feedback. Also, at $z_\mathrm{obs} \lesssim 1$ we find a population with SFHs which quench more slowly than they rise, over $\gtrsim3$ Gyr, which we speculate to be the result of a 'natural' quenching process, where galaxy SFHs die down gradually due to the diminishing overall cosmic gas supply. Purely passive evolution of the quiescent population at $z_\mathrm{obs} \gtrsim 0.5$ is disfavoured by our results.

Subfields: Astronomy and Astrophysics, Instrumentation

View Paper: https://www.semanticscholar.org/paper/fba1952b9ab3bac6129ae8ff148f2a021429d6f2

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Citations in the corpus

How to Measure Galaxy Star Formation Histories. II. Nonparametric Models (2018, background): “…al. 2008; Maraston et al. 2010; Papovich et al. 2011), lognormals (Gladders et al. 2013; Abramson et al. 2015; Diemer et al. 2017), double power-laws (Carnall et al. 2018), and exponentially declining SFHs modified to decouple late-time and early-time SFRs (Simha et al. 2014; Ciesla et al. 2016).”
A Long Time Ago in a Galaxy Far, Far Away: A Candidate z ∼ 12 Galaxy in Early JWST CEERS Imaging (2022, methodology): “PROSPECTOR finds z= - +11.8 0.3 0.2, BAGPIPES finds z= - +11.6 0.2 0.2, CIGALE finds z= - +11.8 0.4 0.4, and DENSE BASIS finds z = - +11.9 0.4 0.4.”
The evolution of the galaxy UV luminosity function at redshifts z ≃ 8 – 15 from deep JWST and ground-based near-infrared imaging (2022, methodology): “In order to constrain the physical properties of this galaxy, we fit our photometric data using the Bagpipes spectral fitting code (Carnall et al. 2018).”
Stellar Population Inference with Prospector (2020, methodology): “…previously by adding additional components (i.e. recent bursts), by choosing more flexible forms for the parametric SFH (e.g., Salim et al. 2007; Carnall et al. 2018), or using a library of SFHs based on semi-analytic or hydrodynamic simulations (e.g., da Cunha et al. 2008; Pacifici et al.…”
An Older, More Quiescent Universe from Panchromatic SED Fitting of the 3D-HST Survey (2018, methodology): “Classic grid-based models grow exponentially in size with model dimensionality, but gridless “on-the-fly” models combined with Markov chain Monte Carlo (MCMC) algorithms can efficiently explore highdimensional (N7) spaces (Chevallard & Charlot 2016; Leja et al. 2017; Carnall et al. 2018).”
Setting the Stage for Cosmic Chronometers. II. Impact of Stellar Population Synthesis Models Systematics and Full Covariance Matrix (2020, background): “…combination, we consider a range zf = [1.5 − 5], in agreement with observational constraints (Franx et al. 2003; Cimatti et al. 2004; McCarthy et al. 2004; Daddi et al. 2005; Treu et al. 2005; Renzini 2006; Pozzetti et al. 2010; Thomas et al. 2010; McDermid et al. 2015; Carnall et al. 2018, 2019).”