Constraints on galaxy formation from the cosmic-far-infrared-background – optical-imaging cross-correlation using Herschel and UNIONS
Bibliographic record
Abstract
Using $\it{Herschel}$-SPIRE imaging and the Canada-France Imaging Survey (CFIS) Low Surface Brightness data products from the Ultraviolet Near-Infrared Optical Northern Survey (UNIONS), we present a cross-correlation between the cosmic infrared background and cosmic optical background fluctuations. With a combined sky area of $91\,{\rm deg}^2$ and a minimum resolved scale of $18\,$arcsec, the cross-spectrum is measured for two cases: all galaxies are kept in the images; or all individually-detected galaxies are masked to produce `background' maps. We report the detection of the cross-correlation signal at $\gtrsim 22\,\sigma$ ($\gtrsim 18\,\sigma$ for the background map). The part of the optical brightness variations that are correlated with the submm emission translates to an rms brightness of $\simeq 32.5\,{\rm mag}\,{\rm arcsec}^{-2}$ in the $r$ band, a level normally unreachable for individual sources. A critical issue is determining what fraction of the cross-power spectrum might be caused by emission from Galactic cirrus. For one of the fields, the Galactic contamination is approximately a factor of 10 higher than the extragalactic signal, with the contamination being estimated using a linear regression from several external survey maps; however, for the other fields, the contamination is typically around 20 per cent. An additional discriminant is that the cross-power spectrum is of the approximate form $P(k)\propto 1/k$, much shallower than that of Galactic cirrus. We interpret the results in a halo-model framework, which shows good agreement with independent measurements for the scalings of star-formation rates in galaxies. The approach presented in this study holds great promise for future surveys such as FYST/CCAT-prime combined with ${\it Euclid}$ or the Vera Rubin Observatory (LSST), which will enable a detailed exploration of the evolution of star formation in galaxies.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".