CONFIRMING THE EXISTENCE OF A QUIESCENT GALAXY POPULATION OUT TO z = 3: A STACKING ANALYSIS OF MID-, FAR-INFRARED, AND RADIO DATA
Bibliographic record
Abstract
ABSTRACT We performed a comprehensive stacking analysis on ∼14,200 quiescent galaxy (QG) candidates at z = 0–3 across mid-, far-infrared (MIR and FIR), and radio wavelengths. Identified via their rest-frame NUV − r and r − J colors, the QG candidates ( ) have drastically different IR and radio properties depending on their 24 μm emission strength. The fraction of QG candidates with strong 24 μm emission (equivalent to inferred star formation rates SFR , hereafter “IR-bright”) increases with redshift and peaks at 15%, and their stacked MIPS 24 μm, Herschel (PACS and SPIRE) and VLA emissions are consistent with being star-forming galaxies (SFGs). In contrast, the majority of QG candidates are faint or undetected at 24 μm individually (i.e., SFR24 < 100 M ⊙ yr−1, hereafter “IR-faint”). Their low dust-obscured SFRs derived from Herschel stacking (SFRH ≲ 3, 15, 50 M ⊙ yr−1 out to z ∼ 1, 2, 3) are >2.5–12.5× lower than compared to SFGs. This is consistent with the quiescence, as expected from their low unobscured SFRs, as inferred from modeling their ultraviolet-to-NIR photometry. The discrepancy between the L IR derived from stacking Herschel and 24 μm indicates that IR-faint QGs have dust SEDs that are different from those of SFGs. For the most massive ( ) IR-faint QGs at z < 1.5, the stacked 1.4 GHz emission is in excess of that expected from other SFR indicators, suggesting a widespread presence of low-luminosity active galactic nuclei. Our results reaffirm the existence of a significant population of QGs out to z = 3, thus corroborating the need to quench star formation in galaxies at early 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".