Quenching or Bursting: The Role of Stellar Mass, Environment, and Specific Star Formation Rate to
Bibliographic record
Abstract
Abstract Using a novel approach, we study the quenching and bursting of galaxies as a function of stellar mass (M *), local environment (Σ), and specific star formation rate (sSFR) using a large spectroscopic sample of ∼123,000 GALEX/SDSS and ∼420 GALEX/COSMOS/LEGA-C galaxies to z ∼ 1. We show that out to z ∼ 1 and at fixed sSFR and local density, on average, less massive galaxies are quenching, whereas more massive systems are bursting, with a quenching/bursting transition at and likely a short quenching/bursting timescale (≲300 Myr). We find that much of the bursting of star formation happens in massive ( ), high-sSFR galaxies (log(sSFR/Gyr−1) ≳ −2), particularly those in the field (log(Σ/Mpc−2) ≲0 and, among group galaxies, satellites more than centrals). Most of the quenching of star formation happens in low-mass ( ), low-sSFR galaxies (log(sSFR/Gyr−1) ≲ −2), in particular those located in dense environments (log(Σ/Mpc−2) ≳1), indicating the combined effects of M * and Σ in the quenching/bursting of galaxies since z ∼ 1. However, we find that stellar mass has stronger effects than environment on the recent quenching/bursting of galaxies to z ∼ 1. At any given M *, sSFR, and environment, centrals are quenchier (quenching faster) than satellites in an average sense. We also find evidence for the strength of mass and environmental quenching being stronger at higher redshift. Our preliminary results have potential implications for the physics of quenching/bursting in galaxies across cosmic time.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".