A New Census of the 0.2 < z < 3.0 Universe. II. The Star-forming Sequence

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

Research output: Contribution to journalArticlepeer-review

45 Scopus citations

Abstract

Abstract We use the panchromatic spectral energy distribution (SED)-fitting code Prospector to measure the galaxy logM*-logSFR relationship (the star-forming sequence) across 0.2 < z < 3.0 using the COSMOS-2015 and 3D-HST UV-IR photometric catalogs. We demonstrate that the chosen method of identifying star-forming galaxies introduces a systematic uncertainty in the inferred normalization and width of the star-forming sequence, peaking for massive galaxies at ∼0.5 and ∼0.2 dex, respectively. To avoid this systematic, we instead parameterize the density of the full galaxy population in the logM*-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 (SFRs) than typical UV+IR formulae. We introduce a simple formalism to understand the difference between SFRs inferred from SED 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 < z < 3. The fully trained normalizing flow including a nonparametric description of ρ ( log M * , logSFR , z ) is available online 2020 https://github.com/jrleja/sfs_leja_trained_flow to facilitate straightforward comparisons with future work.

Original languageEnglish (US)
Article number165
JournalAstrophysical Journal
Volume936
Issue number2
DOIs
StatePublished - Sep 1 2022

All Science Journal Classification (ASJC) codes

  • Astronomy and Astrophysics
  • Space and Planetary Science

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