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
Abstract
Abstract We use the panchromatic spectral energy distribution (SED)-fitting code Prospector to measure the galaxy log M *–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 log M *–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 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>ρ</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mi>log</mml:mi> <mml:msup> <mml:mrow> <mml:mi>M</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>*</mml:mo> </mml:mrow> </mml:msup> <mml:mo>,</mml:mo> <mml:mi>logSFR</mml:mi> <mml:mo>,</mml:mo> <mml:mi>z</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:math> is available online 20 20 https://github.com/jrleja/sfs_leja_trained_flow to facilitate straightforward comparisons with future work.