A New Census of the 0.2 < z < 3.0 Universe, Part II: The Star-Forming Sequence

作     者：Leja, Joel Speagle, Joshua S. Ting, Yuan-Sen Johnson, Benjamin D. Conroy, Charlie Whitaker, Katherine E. Nelson, Erica J. van Dokkum, Pieter Franx, Marijn 

作者机构：Department of Astronomy & Astrophysics The Pennsylvania State University University ParkPA16802 United States Institute for Computational & Data Sciences The Pennsylvania State University University ParkPA United States Institute for Gravitation and the Cosmos The Pennsylvania State University University ParkPA16802 United States Department of Statistical Sciences University of Toronto TorontoONM5S 3G3 Canada David A. Dunlap Department of Astronomy & Astrophysics University of Toronto TorontoONM5S 3H4 Canada Dunlap Institute for Astronomy & Astrophysics University of Toronto TorontoONM5S 3H4 Canada Research School of Astronomy & Astrophysics Australian National University Cotter Rd. Weston ACT2611 Australia Institute for Advanced Study PrincetonNJ08540 United States Department of Astrophysical Sciences Princeton University PrincetonNJ08540 United States Observatories of the Carnegie Institution of Washington 813 Santa Barbara Street PasadenaCA91101 United States Research School of Computer Science Australian National University ActonACT2601 Australia Center for Astrophysics | Harvard & Smithsonian 60 Garden St. CambridgeMA02138 United States Department of Astronomy University of Massachusetts Amherst 710 N Pleasant Street AmherstMA01003 United States  Copenhagen Denmark Astrophysical & Planetary Sciences 391 UCB 2000 Colorado Ave Duane Physics Building Rm. E226 BoulderCO80309 United States Department of Astronomy Yale University New HavenCT06511 United States Leiden Observatory Leiden University LeidenNL-2300 RA Netherlands 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2021年

核心收录：

主　　题：Stars 

摘      要：We use the panchromatic SED-fitting code Prospector to measure the galaxy logM∗-logSFR relationship (the ‘star-forming sequence’) across 0.2 ∗-logSFR-redshift plane using a flexible neural network known as a normalizing flow. The resulting star-forming sequence has a low-mass slope near unity and a much flatter slope at higher masses, with a normalization 0.2 − 0.5 dex lower than typical inferences in the literature. We show this difference is due to the sophistication of the Prospector stellar populations modeling: the nonparametric star formation histories naturally produce higher masses while the combination of individualized metallicity, dust, and star formation history constraints produce lower star formation rates than typical UV+IR formulae. We introduce a simple formalism to understand the difference between SFRs inferred from spectral energy distribution fitting and standard template-based approaches such as UV+IR SFRs. Finally, we demonstrate the inferred star-forming sequence is consistent with predictions from theoretical models of galaxy formation, resolving a long-standing ∼ 0.2 − 0.5 dex offset with observations at 0.5 ∗, log SFR, z) is made available online a) to facilitate straightforward comparisons with future work. Copyright © 2021, The Authors. All rights reserved.