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.

We investigate the timescale over which the infrared (IR) luminosity decreases after a complete and rapid quenching of star formation using observations of local and high-redshift galaxies. From spectral energy distribution modelling, we derive the time since quenching of a subsample of 14 galaxies from the Herschel Reference Survey that suffer from ram-pressure stripping due to the environment of the Virgo cluster and of a subsample of 7 rapidly quenched COSMOS galaxies selected through a state-of-the-art statistical method already tested on the determination of galaxy star formation history (SFH). Three out of the seven COSMOS galaxies have an optical spectrum with no emission line, confirming their quenched nature. We obtained the present physical properties of the combined sample (local plus high-redshift) from the long-term SFH properties, as well as the past LIR of these galaxies just before their quenching. We show that this past LIR is consistent with the LIR of reference samples of normally star-forming galaxies with same stellar mass and redshift as each of our quenched galaxies. We put constraints on the present to past IR luminosity ratio as a function of quenching time. The two samples probe different dynamical ranges in terms of quenching age with the HRS galaxies exhibiting longer timescales (0.2–3 Gyr) compared to the COSMOS ones (< 100 Myr). Assuming an exponential decrease in the LIR after quenching, the COSMOS quenched galaxies are consistent with short e-folding times of less than a couple of hundred million years, while the properties of the HRS quenched galaxies are compatible with larger timescales of several hundred million years. For the HRS sample, this result is consistent with the known quenching mechanism that affected them, namely ram pressure stripping due to the environment. For the COSMOS sample, different quenching processes are acting on short to intermediate timescales. Processes such as galaxy mergers, disk instabilities, and environmental effects can produce such strong star formation variability.