Nonparametric Star Formation History Reconstruction with Gaussian Processes. I. Counting Major Episodes of Star Formation

• Published in: The Astrophysical journal 2019-07, Vol.879 (2), p.116
• Main Authors: Iyer, Kartheik G., Gawiser, Eric, Faber, Sandra M., Ferguson, Henry C., Kartaltepe, Jeyhan, Koekemoer, Anton M., Pacifici, Camilla, Somerville, Rachel S.
• Format: Article
• Language: English
• Subjects: Astrophysics; Galactic evolution; Galactic structure; Galaxies; galaxies: evolution; galaxies: fundamental parameters; galaxies: star formation; galaxies: statistics; Gaussian process; Morphology; Nonparametric statistics; Parameter estimation; Parameterization; Physical properties; Quantiles; Reconstruction; Red shift; Spheroids; Star & galaxy formation; Star formation; Stars & galaxies; Stellar mass; techniques: photometric
• ISSN: 0004-637X; 1538-4357
• DOI: 10.3847/1538-4357/ab2052

The star formation histories (SFHs) of galaxies contain imprints of the physical processes responsible for regulating star formation during galaxy growth and quenching. We improve the Dense Basis SFH reconstruction method of Iyer & Gawiser, introducing a nonparametric description of the SFH based on the lookback times at which a galaxy assembles certain quantiles of its stellar mass. The method uses Gaussian processes to create smooth SFHs independent of any functional form, with a flexible number of parameters that is adjusted to extract the maximum amount of information from the SEDs being fit. Applying the method to reconstruct the SFHs of 48,791 galaxies with H < 25 at 0.5 < z < 3.0 across the five Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey fields, we study the evolution of galaxies over time. We quantify the fraction of galaxies that show multiple major episodes of star formation, finding that the median time between two peaks of star formation is , where tuniv is the age of the universe at a given redshift and remains roughly constant with stellar mass. Correlating SFHs with morphology allows us to compare the timescales on which the SFHs decline for different morphological classifications, ranging from for galaxies with spiral arms to for spheroids at 0.5 < z < 1.0 with 1010 < M* < 1010.5M . The Gaussian process-based SFH description provides a general approach to reconstruct smooth, flexible, nonparametric SFH posteriors for galaxies that can be incorporated into Bayesian SED fitting codes to minimize the bias in estimating physical parameters due to SFH parameterization.