Confronting theoretical models with the observed evolution of the galaxy population out to z= 4
Bibliographic record
Abstract
We construct lightcones for the semi-analytic galaxy formation simulation of Guo et al. (2011) and make mock catalogues for comparison with deep high-redshift surveys. Photometric properties are calculated with two different stellar population synthesis codes (Bruzual & Charlot 2003; Maraston 2005) in order to study sensitivity to this aspect of the modelling. The catalogues are publicly available and include photometry for a large number of observed bands from 4000{\\deg}A to 6{\\mu}m, as well as rest-frame photometry and intrinsic properties of the galaxies. Guo et al. (2011) tuned their model to fit the low-redshift galaxy population but noted that at z > 1 it overpredicts the abundance of galaxies below the "knee" of the stellar mass function. Here we extend the comparison to deep galaxy counts in the B, i, J, K and IRAC 3.6{\\mu}m, 4.5{\\mu}m and 5.8{\\mu}m bands, to the redshift distributions of K and 5.8{\\mu}m selected galaxies, and to the evolution of rest-frame luminosity functions in the B and K bands. The B, i and J counts are well reproduced, but at longer wavelengths the overabundant high-redshift galaxies produce excess faint counts. The predicted redshift distributions for K and 5.8{\\mu}m selected samples highlight the effect of emission from thermally pulsing AGB stars. The full treatment of Maraston (2005) predicts three times as many z~2 galaxies in faint 5.8{\\mu}m selected samples as the model of Bruzual & Charlot (2003), whereas the two models give similar predictions for K-band selected samples. Although luminosity functions are adequately reproduced out to z~3 in rest-frame B, the same is true at rest-frame K only if TP-AGB emission is included, and then only at high luminosity. Fainter than L* the two synthesis models agree but overpredict the number of galaxies, another reflection of the overabundance of ~10^10M\\odot model galaxies at z > 1.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".