Herschel-ATLAS: counterparts from the ultraviolet-near-infrared in the science demonstration phase catalogue★
Bibliographic record
Abstract
We present a technique to identify optical counterparts of 250-μm-selected sources from the Herschel–ATLAS survey. Of the 6621 250 μm > 32-mJy sources in our science demonstration catalogue we find that ∼60 per cent have counterparts brighter than r = 22.4 mag in the Sloan Digital Sky Survey. Applying a likelihood ratio technique we are able to identify 2423 of the counterparts with a reliability R > 0.8. This is approximately 37 per cent of the full 250-μm catalogue. We have estimated photometric redshifts for each of these 2423 reliable counterparts, while 1099 also have spectroscopic redshifts collated from several different sources, including the GAMA survey. We estimate the completeness of identifying counterparts as a function of redshift, and present evidence that 250-μm-selected Herschel–ATLAS galaxies have a bimodal redshift distribution. Those with reliable optical identifications have a redshift distribution peaking at z ≈ 0.25 ± 0.05, while submillimetre colours suggest that a significant fraction with no counterpart above the r-band limit have z > 1. We also suggest a method for selecting populations of strongly lensed high-redshift galaxies. Our identifications are matched to UV–NIR photometry from the GAMA survey, and these data are available as part of the Herschel–ATLAS public data release.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.041 |
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".