TP-AGB stars and stellar population properties of a post-starburst galaxy at <i>z</i> ∼ 2 through optical and near-infrared spectroscopy with JWST
Bibliographic record
Abstract
We present a detailed optical and near-IR (NIR) spectral analysis of J-138717, a post-starburst galaxy at z = 1.8845 observed with JWST/NIRSpec, for which we derive a stellar mass of 3.5±0.2×1010 M⊙ and a stellar velocity dispersion of 198±10 km s−1. We estimate an age of ∼0.9 Gyr and a subsolar metallicity (between −0.4 and −0.2 dex). We find generally consistent results when we fit the optical and NIR wavelength ranges separately or with different model libraries. The reconstruction of the star formation history indicates that the galaxy assembled most of its mass quickly and then quenched rapidly, ∼0.4 Gyr before the observation. Line diagnostics suggest that the weak emission is probably powered by residual star formation (star formation rate ∼0.2 M⊙ yr−1) or a low-luminosity active galactic nucleus, without strong evidence for outflows in ionized or neutral gas. We performed a detailed study of the NIR spectral indices by comparing observations with predictions of several current stellar population models. This is unprecedented at this high redshift. In particular, the analysis of several CO and CN features argues against a strong contribution of thermally pulsating (TP) asymptotic giant branch (AGB) stars. The observations agree better with models that include very little contribution from TP-AGB stars, but they are also consistent with a mild contribution from TP-AGB stars when a younger age, consistent with the fits, is assumed. The analysis of other NIR spectral indices shows that current models struggle to reproduce the observations. This highlights the need for improved stellar population models in the NIR, especially at young ages and low metallicities. This is most relevant for studying high-redshift galaxies in the era of the JWST.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".