A spectroscopic measurement of galaxy formation time-scales with the Redshift One LDSS3 Emission line Survey
Bibliographic record
Abstract
We present measurements of the specific star-formation rate (SSFR)-stellar mass relation for star-forming galaxies. Our deep spectroscopic samples are based on the Redshift One LDSS3 Emission line Survey, ROLES, and European Southern Observatory, ESO, public spectroscopy at z=1, and on the Sloan Digital Sky Survey (SDSS) at z=0.1. These datasets cover an equally deep mass range of 8.5<~log(M*/Msun)<~11 at both epochs. We find that the SSFR--mass relation evolves in a way which is remarkably independent of stellar mass, as we previously found for the star-formation rate density (SFRD)--mass relation. At higher masses, such as those probed by previous surveys, the evolution in SSFR--mass is almost independent of stellar mass. At higher masses (log(M*/Msun)>10) the shapes of the cumulative cosmic SFRDs are very similar at both z=0.1 and z=1.0, both showing 70% of the total SFRD above a mass of log(M*/Msun)>10. Mass functions are constructed for star-forming galaxies and found to evolve by only <35% between z=1 and z=0.1 over the whole mass range. The evolution is such that the mass function decreases with increasing cosmic time, confirming that galaxies are leaving the star-forming sequence/blue cloud. The observational results are extended to z~2 by adding two recent Lyman break galaxy samples, and data at these three epochs (z=0.1, 1, 2) are compared with the GALFORM semi-analytic model of galaxy formation. GALFORM predicts an overall SFR density (SFRD) as a function of stellar mass in reasonable agreement with the observations. The star formation timescales inferred from 1/SSFR also give reasonable overall agreement, with the agreement becoming worse at the lowest and highest masses. The models do not reproduce the SSFR upturn seen in our data at low masses, where the effects of extinction and active galactic nuclei feedback should be minimal and the comparison should be most robust.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".