Toward Precise Galaxy Evolution: A Comparison between Spectral Indices of z ∼1 Galaxies in the IllustrisTNG Simulation and the LEGA-C Survey
Bibliographic record
Abstract
Abstract We present the first comparison of observed stellar continuum spectra of high-redshift galaxies and mock galaxy spectra generated from hydrodynamical simulations. The mock spectra are produced from the IllustrisTNG TNG100 simulation combined with stellar population models and take into account dust attenuation and realistic observational effects (aperture effects and noise). We compare the simulated Dn4000 and EW(Hδ) of galaxies with 10.5 ≤ log ( M * / M ⊙ ) ≤ 11.5 at 0.6 ≤ z ≤ 1.0 to the observed distributions from the LEGA-C survey. TNG100 globally reproduces the observed distributions of spectral indices, implying that the age distribution of galaxies in TNG100 is generally realistic. Yet there are small but significant differences. For old galaxies, TNG100 shows small Dn4000 when compared to LEGA-C, while LEGA-C galaxies have larger EW(Hδ) at fixed Dn4000 . There are several possible explanations: (1) LEGA-C galaxies have overall older ages combined with small contributions (a few percent in mass) from younger (<1 Gyr) stars, while TNG100 galaxies may not have such young subpopulations; (2) the spectral mismatch could be due to systematic uncertainties in the stellar population models used to convert stellar ages and metallicities to observables. In conclusion, the latest cosmological galaxy-formation simulations broadly reproduce the global age distribution of galaxies at z ∼ 1 and, at the same time, the high quality of the latest observed and simulated data sets help constrain stellar population synthesis models as well as the physical models underlying the simulations.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".