A Census of Star Formation Histories of Massive Galaxies at 0.6 < z < 1 from Spectrophotometric Modeling Using Bagpipes and Prospector
Bibliographic record
Abstract
Abstract We present individual star formation histories (SFHs) of ∼3000 massive galaxies (log(M */M ⊙) > 10.5) from the Large Early Galaxy Astrophysics Census spectroscopic survey at a lookback time of ∼7 billion yr and quantify the population trends leveraging 20 hr deep-integrated spectra of these ∼1800 star-forming and ∼1200 quiescent galaxies at 0.6 < z < 1.0. Essentially all galaxies at this epoch contain stars of age <3 Gyr, in contrast with older massive galaxies today, facilitating better recovery of previous generations of star formation at cosmic noon and earlier. We conduct spectrophotometric analysis using parametric and nonparametric Bayesian stellar population synthesis modeling tools—Bagpipes and Prospector—to constrain the median SFHs of this mass complete sample and characterize population trends. A consistent picture arises for the late-time stellar mass growth when quantified as t 50 and t 90, corresponding to the age of the Universe when galaxies formed 50% and 90% of their total stellar mass, although the two methods disagree at the earliest formation times (e.g., t 10). Our results reveal trends in both stellar mass and stellar velocity dispersion as in the local Universe—low-mass galaxies with shallower potential wells grow their stellar masses later in cosmic history compared to high-mass galaxies. Unlike local quiescent galaxies, the median duration of late-time star formation (τ SF,late = t 90–t 50) does not consistently depend on the stellar mass. This census sets a benchmark for future deep spectrophotometric studies of the more distant Universe.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".