Evolutionary synthesis of galaxies at high spectral resolution with the code PEGASE-HR
Bibliographic record
Abstract
We present PEGASE-HR, a new stellar population synthesis program generating high resolution spectra () over the optical range = 400–680 nm. It links the spectro-photometric model of galaxy evolution PEGASE.2 (Fioc & Rocca-Volmerange 1997) to an updated version of the ELODIE library of stellar spectra observed with the 193 cm telescope at the Observatoire de Haute-Provence [CITE]. The ELODIE star set gives a fairly complete coverage of the Hertzprung-Russell (HR) diagram and makes it possible to synthesize populations in the range to +0.4. This code is an exceptional tool for exploring signatures of metallicity, age, and kinematics. We focus on a detailed study of the sensitivity to age and metallicity of the high-resolution stellar absorption lines and of the classical metallic indices proposed until now to solve the age-metallicity degeneracy. Validity tests on several stellar lines are performed by comparing our predictions for Lick indices to the models of other groups. The comparison with the lower resolution library BaSeL [CITE] confirms the quality of the ELODIE library when used for single stellar populations (SSPs) from 107 to yr. Predictions for the evolved populations of globular clusters and elliptical galaxies are given and compared to observational data. Two new high-resolution indices are proposed around the Hγ line. They should prove useful in the analysis of spectra from the new generation of telescopes and spectrographs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.010 | 0.005 |
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".