A New Census of the 0.2 < z < 3.0 Universe. II. The Star-forming Sequence
Bibliographic record
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".