paper

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

arXiv:2110.04314 · doi:10.3847/1538-4357/ac887d

Abstract

We use the panchromatic SED-fitting code Prospector to measure the galaxy logM-logSFR relationship (the `star-forming sequence') across 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 dex and 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 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 dex offset with observations at . The fully trained normalizing flow including a nonparametric description of is made available online to facilitate straightforward comparisons with future work.

Accepted to ApJ. Trained flow available at https://github.com/jrleja/sfs_leja_trained_flow

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