Have we seen all the galaxies that comprise the cosmic infrared background at 250 μm ≤λ ≤ 500 μm?
Bibliographic record
Abstract
Abstract The cosmic infrared background (CIB) provides a fundamental observational constraint on the star-formation history of galaxies over cosmic history. We estimate the contribution to the CIB from catalogued galaxies in the COSMOS field by using a novel map fitting technique on the Herschel SPIRE maps. Prior galaxy positions are obtained using detections over a large range in wavelengths in the Ks–3 GHz range. Our method simultaneously fits the galaxies, the system foreground, and the leakage of flux from galaxies located in masked areas and corrects for an “over-fitting” effect not previously accounted for in stacking methods. We explore the contribution to the CIB as a function of galaxy survey wavelength and depth. We find high contributions to the CIB with the deep r (mAB ≤ 26.5), Ks (mAB ≤ 24.0) and 3.6 μm (mAB ≤ 25.5) catalogues. We combine these three deep catalogues and find a total CIB contributions of 10.5 ± 1.6, 6.7 ± 1.5 and 3.1 ± 0.7 nWm−2sr−1 at 250, 350 and 500 μm, respectively. Our CIB estimates are consistent with recent phenomenological models, prior based SPIRE number counts and with (though more precise than) the diffuse total measured by FIRAS. Our results raise the interesting prospect that the CIB contribution at λ ≤ 500 μm from known galaxies has converged. Future large-area surveys like those with the Large Synoptic Survey Telescope are therefore likely to resolve a substantial fraction of the population responsible for the CIB at 250 μm ≤λ ≤ 500 μm.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".