MULTI-WAVELENGTH STUDY OF A COMPLETE IRAC 3.6 μm SELECTED GALAXY SAMPLE: A FAIR CENSUS OF RED AND BLUE POPULATIONS AT REDSHIFTS 0.4-1.2
Bibliographic record
Abstract
We present a multi-wavelength study of a 3.6 μm selected galaxy sample in the Extended Groth Strip (EGS). The sample is complete for galaxies with stellar mass >10 9.5 M ☉ and redshift 0.4 < z < 1.2. In this redshift range, the Infrared Array Camera 3.6 μm band measures the rest-frame near-infrared band, permitting nearly unbiased selection with respect to both quiescent and star-forming galaxies. The numerous spectroscopic redshifts available in the EGS are used to train an artificial neural network to estimate photometric redshifts. The distribution of photometric redshift errors is Gaussian with standard deviation ∼0.025(1 + z ), and the fraction of redshift failures (>3σ errors) is about 3.5%. A new method of validation based on pair statistics confirms the estimate of standard deviation even for galaxies lacking spectroscopic redshifts. Basic galaxy properties measured include rest-frame U − B colors, B - and K -band absolute magnitudes, and stellar masses. We divide the sample into quiescent and star-forming galaxies according to their rest-frame U − B colors and 24–3.6 μm flux density ratios and derive rest K -band luminosity functions and stellar mass functions for quiescent, star-forming, and all galaxies. The results show that massive, quiescent galaxies were in place by z ≈ 1, but lower mass galaxies generally ceased their star formation at later epochs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".