Star Formation Histories of z ∼ 1 Galaxies in LEGA-C
Bibliographic record
Abstract
Abstract Using high-resolution spectra from the VLT Large Early Galaxy Astrophysics Census (LEGA-C) program, we reconstruct the star formation histories (SFHs) of 607 galaxies at redshifts z = 0.6–1.0 and stellar masses ≳1010 M ⊙ using a custom full spectrum fitting algorithm that incorporates the emcee and FSPS packages. We show that the mass-weighted age of a galaxy correlates strongly with stellar velocity dispersion (σ *) and ongoing star formation (SF) activity, with the stellar content in higher-σ * galaxies having formed earlier and faster. The SFHs of quiescent galaxies are generally consistent with passive evolution since their main SF epoch, but a minority show clear evidence of a rejuvenation event in their recent past. The mean age of stars in galaxies that are star-forming is generally significantly younger, with SF peaking after z < 1.5 for almost all star-forming galaxies in the sample: many of these still have either constant or rising SFRs on timescales >100 Myr. This indicates that z > 2 progenitors of z ∼ 1 star-forming galaxies are generally far less massive. Finally, despite considerable variance in the individual SFHs, we show that the current SF activity of massive galaxies (>L *) at z ∼ 1 correlates with SF levels at least 3 Gyr prior: SFHs retain “memory” on a large fraction of the Hubble time. Our results illustrate a novel approach to resolve the formation phase of galaxies, and, by identifying their individual evolutionary paths, one can connect progenitors and descendants across cosmic time. This is uniquely enabled by the high-quality continuum spectroscopy provided by the LEGA-C survey.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".