Constraining the recent star formation history of galaxies: an approximate Bayesian computation approach

Abstract

Although galaxies are found to follow a tight relation between their star formation rate and stellar mass, they are expected to exhibit complex star formation histories (SFH), with short-term fluctuations. The goal of this pilot study is to present a method that will identify galaxies that are undergoing a strong variation of star formation activity in the last tens to hundreds Myr. In other words, the proposed method will determine whether a variation in the last few hundreds of Myr of the SFH is needed to properly model the SED rather than a smooth normal SFH. To do so, we analyze a sample of COSMOS galaxies with 0.5 < z < 1 and log M * > 8.5 using high signal-to-noise ratio broad band photometry. We apply Approximate Bayesian Computation, a state-of-the-art statistical method to perform model choice, associated to machine learning algorithms to provide the probability that a flexible SFH is preferred based on the observed flux density ratios of galaxies. We present the method and test it on a sample of simulated SEDs. The input information fed to the algorithm is a set of broadband UV to NIR (rest-frame) flux ratios for each galaxy. The choice of using colors is made to remove any difficulty linked to normalization when using classification algorithms. The method has an error rate of 21% in recovering the right SFH and is sensitive to SFR variations larger than 1 dex. A more traditional SED fitting method using CIGALE is tested to achieve the same goal, based on fits comparisons through Bayesian Information Criterion but the best error rate obtained is higher, 28%. We apply our new method to the COSMOS galaxies sample. The stellar mass distribution of galaxies with a strong to decisive evidence against the smooth delayed-τ SFH peaks at lower M * compared to galaxies where the smooth delayed-τ SFH is preferred. We discuss the fact that this result does not come from any bias due to our training. Finally, we argue that flexible SFHs are needed to be able to cover that largest SFR-M * parameter space possible.

Investigating the delay between dust radiation and star-formation in local and distant quenching galaxies

“…For these sources, the addition of an extra flexibility in the recent SFH is needed to better recover the SFR. This was further confirmed by Aufort et al (2020) from a statistical approach. As in this paper we are only focusing on the latest SFH variation, we rely on these studies and use a delayed SFH associated with the recent flexibility, as presented and described in Ciesla et al (2017) and Aufort et al (2020).…”

“…To select the high-redshift galaxy sample, we rely on the results of Aufort et al (2020) who aimed to identify galaxies having experienced a rapid and drastic variation of their star formation activity in the last 500 Myr from a sample of COSMOS galaxies (Laigle et al 2016) with good-quality data S /N >10. These authors selected a sample of galaxies from the COSMOS sample with 0.5 < z < 1, stellar mass larger than 10 8.5 M , and high-S/N flux densities.…”

“…They developed a method based on approximate Bayesian computation (ABC; see, e.g., Marin et al 2012;Sisson et al 2018) combined with a machine-learning algorithm (XGBoost, see Chen & Guestrin 2016) to compute the probability that a galaxy experienced a recent (less than 500 Myr) and drastic variation of its star formation activity that could be either an enhancement of the SFR or quenching. Based on the observed SED of a galaxy, Aufort et al (2020) chose the most appropriate SFH between a finite set. The main idea behind ABC is to rely on many simulated SEDs generated from all the SFHs in competition using parameters drawn from the prior.…”

“…Of the 12 380 galaxies of their sample, 376 galaxies have a posterior probability higher than 0.91. Although the results of Aufort et al (2020) allow us to select galaxies with a recent variation of SFH, their method does not provide information on the nature of this variation, that is, whether a galaxy underwent a starburst phase or a quenching of SF. For the purpose of this study, we need to select galaxies that underwent a strong decrease in SFR.…”

“…We tested several runs of SED fitting whilst varying these three parameters: (n ISM , n BC , µ) combinations of (−0,7; −0.7; 0.3), (−1; −1; 0.3), and (−0.7, −1.3; 0.3), and the same combinations with µ variable (0.2, 0.3, 0.4). For each test and for the quenched candidates, we compared the quality of the new fit to the one proposed in our paper using the Bayesian information criterion (see e.g., Ciesla et al 2018;Aufort et al 2020). The fits with different combinations did not result in a better quality fit than the one adopted here.…”