HerMES: detection of cosmic magnification of submillimetre galaxies using angular cross-correlation★
Bibliographic record
Abstract
Cosmic magnification is due to the weak gravitational lensing of sources in the distant Universe by foreground large-scale structure leading to coherent changes in the observed number density of the background sources. Depending on the slope of the background source number counts, cosmic magnification causes a correlation between the background and foreground galaxies, which is unexpected in the absence of lensing if the two populations are spatially disjoint. Previous attempts using submillimetre (submm) sources have been hampered by small number statistics. The large number of sources detected in the Herschel Multi-tiered Extra-galactic Survey (HerMES) Lockman-Spitzer Wide-area Infrared Extragalactic (SWIRE) field enables us to carry out the first robust study of the cross-correlation between submm sources and sources at lower redshifts. Using ancillary data, we compile two low-redshift samples from Sloan Digital Sky Survey (SDSS) and SWIRE with 〈z〉∼ 0.2 and 0.4, respectively, and cross-correlate with two submm samples based on flux density and colour criteria, selecting galaxies preferentially at z∼ 2. We detect cross-correlation on angular scales between ∼1 and 50 arcmin and find clear evidence that this is primarily due to cosmic magnification. A small, but non-negligible signal from intrinsic clustering is likely to be present due to the tails of the redshift distribution of the submm sources overlapping with those of the foreground samples.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| 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) | 0.002 | 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".