Σ<sub>SFR</sub>–M <sub>∗</sub> Diagram: A Valuable Galaxy Evolution Diagnostic to Complement (s)SFR–M <sub>∗</sub> Diagrams
Bibliographic record
Abstract
Abstract The specific star formation rate (sSFR) is commonly used to describe the level of galaxy star formation (SF) and to select quenched galaxies. However, since it is a relative measure of the young-to-old population, an ambiguity in its interpretation may arise because a low sSFR can be due to either a substantial previous mass buildup or SF activity that is low. We show, using large samples spanning 0 < z < 2, that the normalization of the star formation rate (SFR) by the physical extent over which SF is taking place (i.e., the SFR surface density, Σ SFR ) overcomes this ambiguity. Σ SFR has a strong physical basis, being tied to the molecular gas density and the effectiveness of stellar feedback, so we propose Σ SFR – M * as an important galaxy evolution diagram to complement (s)SFR– M * diagrams. Using the Σ SFR – M * diagram we confirm the Schiminovich et al. result that the level of SF along the main sequence today is only weakly mass-dependent—high-mass galaxies, despite their redder colors, are as active as blue, low-mass ones. At higher redshift, the slope of the “Σ SFR main sequence” steepens, signaling the epoch of bulge buildup in massive galaxies. We also find that Σ SFR based on the optical isophotal radius more cleanly selects both starbursting and spheroid-dominated (early-type) galaxies than the sSFR. One implication of our analysis is that the assessment of the inside-out versus outside-in quenching scenarios should consider both sSFR and Σ SFR radial profiles, because ample SF may be present in bulges with low sSFRs (red color).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".