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 identifies galaxies that undergo strong variation in star formation activity in the last ten to some hundred million years. In other words, the proposed method determines whether a variation in the last few hundred million years of the SFH is needed to properly model the spectral energy distribution (SED) rather than a smooth normal SFH. To do so, we analyzed a sample of COSMOS galaxies with 0.5 <  z <  1 and log M* >  8.5 using high signal-to-noise ratio broadband photometry. We applied approximate Bayesian computation, a custom statistical method for performing model choice, which is associated with 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 classification algorithms are used. The method has an error rate of 21% in recovering the correct 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 fit comparisons through the Bayesian information criterion, but the best error rate we obtained is higher, 28%. We applied 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* than for 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 the largest possible SFR-M* parameter space.