HerMES: THE CONTRIBUTION TO THE COSMIC INFRARED BACKGROUND FROM GALAXIES SELECTED BY MASS AND REDSHIFT
Bibliographic record
Abstract
We quantify the fraction of the cosmic infrared background (CIB) that originates from galaxies identified in the UV/optical/near-infrared by stacking 81,250 (35.7 arcmin -2 ) K-selected sources (K AB < 24.0) split according to their rest-frame U -V versus V -J colors into 72,216 star-forming and 9034 quiescent galaxies, on maps from Spitzer/MIPS (24 m), Herschel/PACS (100, 160 m), Herschel/SPIRE (250, 350, 500 m), and AzTEC (1100 m). The fraction of the CIB resolved by our catalog is (69% 15%) at 24 m, (78% 17%) at 70 m, (58% 13%) at 100 m, (78% 18%) at 160 m, (80% 17%) at 250 m, (69% 14%) at 350 m, (65% 12%) at 500 m, and (45% 8%) at 1100 m. Of that total, about 95% originates from star-forming galaxies, while the remaining 5% is from apparently quiescent galaxies. The CIB at 200 m appears to be sourced predominantly from galaxies at z 1, while at 200 m the bulk originates from 1 z 2. Galaxies with stellar masses log(M/M ) = 9.5-11 are responsible for the majority of the CIB, with those in the log(M/M ) = 9.5-10 bin contributing mostly at < 250 m, and those in the log(M/M ) = 10-11 bin dominating at > 350 m. The contribution from galaxies in the log(M/M ) = 9.0-9.5 (lowest) and log(M/M ) = 11.0-12.0 (highest) stellarmass bins contribute the least-both of order 5%-although the highest stellar-mass bin is a significant contributor to the luminosity density at z 2. The luminosities of the galaxies responsible for the CIB shifts from combinations of "normal" and luminous infrared galaxies (LIRGs) at 160 m, to LIRGs at 160 500 m, to finally LIRGs and ultra-luminous infrared galaxies at 500 m. Stacking analyses were performed using simstack, a novel algorithm designed to account for possible biases in the stacked flux density due to clustering. It is made available to the public at www.astro.caltech.edu/viero/viero_homepage/toolbox.html.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".