4.7 Article

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

期刊

ASTROPHYSICAL JOURNAL
卷 879, 期 2, 页码 -

出版社

IOP PUBLISHING LTD
DOI: 10.3847/1538-4357/ab2052

关键词

galaxies: evolution; galaxies: fundamental parameters; galaxies: star formation; galaxies: statistics techniques:photometric

资金

  1. Rutgers University
  2. Rutgers
  3. State of New Jersey
  4. Simons Foundation
  5. NASA from the Space Telescope Science Institute [HST-AR-14564.001-A, GO-12060]
  6. NASA [NAS5-26555]

向作者/读者索取更多资源

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 similar to 0.42(-0.10)(+0.15) t(univ) Gyr where t(univ) 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 0.60(-0.50)(+1.54) Gyr for galaxies with spiral arms to 2.50(+2.25)(-1.50) for spheroids at 0.5. < z < 1.0 with 10(10) < M-* < 10(10.5)M(circle dot). 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.

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