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

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 similar to 0.5 and similar to 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 similar to 0.2-0.5 dex offset with observations at 0.5 < z < 3. The fully trained normalizing flow including a nonparametric description of rho(logM*,logSFR,z) is available online(20) to facilitate straightforward comparisons with future work.

Automated summary

In this study, we use the panchromatic spectral energy distribution-fitting code Prospector to measure the relationship between galaxy mass and star formation rate (SFR). We find that the chosen method of identifying star-forming galaxies introduces uncertainty in the inferred normalization and width of the star-forming sequence. To address this, we use a flexible neural network known as a normalizing flow to parameterize the density of the full galaxy population. The resulting star-forming sequence has a different slope and normalization compared to previous inferences.