Toward a Better Understanding of Cosmic Chronometers: Stellar Population Properties of Passive Galaxies at Intermediate Redshift
Bibliographic record
Abstract
Abstract We take advantage of the publicly available LEGA-C spectroscopic survey to measure the stellar population properties of 140 individual massive and passive galaxies at z ∼ 0.7. We develop and publicly release PyLick, a flexible Python code to measure UV to near-IR spectral indices. With PyLick we study the H/K ratio as a new diagnostic based on the pseudo-Lick Ca ii H and K indices and find that a cut in H/K < 1.1 can be used jointly with other criteria to select (or verify the purity of) samples of passive galaxies. By combining photometric and spectroscopic criteria, we select a reliable sample of passively evolving galaxies. We constrain single-burst stellar ages, metallicities [Z/H], and [α/Fe] with an optimized set of Lick indices, exploring in detail the robustness of our measurement against different combinations. Even without imposing cosmological priors, the derived ages follow a clear trend compatible with the expected cosmological aging of the universe. We observe no significant redshift evolution for the metal abundance with respect to the values derived at z = 0, with median [Z/H] = 0.08 ± 0.18 and [α/Fe] = 0.13 ± 0.11. Finally, we analyze the relations between log age, [Z/H], [α/Fe], and the stellar velocity dispersion, finding slopes of (0.5 ± 0.1), (0.3 ± 0.2), and (0.2 ± 0.1), respectively; the small scatter of <0.2 dex points to rather homogeneous and short star formation histories. Overall, these results confirm and extend low-redshift findings of a mass-downsizing evolution. This work further strengthens the possibility of selecting pure samples of passive galaxies to be exploited reliably as cosmic chronometers to place independent cosmological constraints.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".