Papers › A New Census of the 0.2< z <3.0 Universe, Part II: The Star-Forming Sequence
A New Census of the 0.2< z <3.0 Universe, Part 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
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We use the panchromatic 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 dex 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 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<z<3. The fully trained normalizing flow including a nonparametric description of ρ(logM^*,logSFR,z) is made available online to facilitate straightforward comparisons with future work.
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