THE SCUBA-2 COSMOLOGY LEGACY SURVEY: MULTIWAVELENGTH COUNTERPARTS TO 10<sup>3</sup> SUBMILLIMETER GALAXIES IN THE UKIDSS-UDS FIELD
Bibliographic record
Abstract
ABSTRACT We present multiwavelength identifications for the counterparts of 1088 submillimeter sources detected at 850 μm in the SCUBA-2 Cosmology Legacy Survey study of the UKIRT Infrared Deep Sky Survey-Ultra-Deep Survey (UDS) field. By utilizing an Atacama Large Millimeter Array (ALMA) pilot study on a subset of our bright SCUBA-2 sample as a training set, along with the deep optical–near-infrared (OIR) data available in this field, we develop a novel technique, Optical–IR Triple Color (OIRTC), using z − K, K − [3.6], [3.6] − [4.5] colors to select the candidate submillimeter galaxy (SMG) counterparts. By combining radio identification and the OIRTC technique, we find counterpart candidates for 80% of the Class = 1 ≥ 4σ SCUBA-2 sample, defined as those that are covered by both radio and OIR imaging and the base sample for our scientific analyses. Based on the ALMA training set, we expect the accuracy of these identifications to be 82% ± 20%, with a completeness of 69% ± 16%, essentially as accurate as the traditional p-value technique but with higher completeness. We find that the fraction of SCUBA-2 sources having candidate counterparts is lower for fainter 850 μm sources, and we argue that for follow-up observations sensitive to SMGs with S 850 ≳ 1 mJy across the whole ALMA beam, the fraction with multiple counterparts is likely to be >40% for SCUBA-2 sources at S 850 ≳ 4 mJy. We find that the photometric redshift distribution for the SMGs is well fit by a lognormal distribution, with a median redshift of z = 2.3 ± 0.1. After accounting for the sources without any radio and/or OIRTC counterpart, we estimate the median redshift to be z = 2.6 ± 0.1 for SMGs with S 850 > 1 mJy. We also use this new large sample to study the clustering of SMGs and the far-infrared properties of the unidentified submillimeter sources by stacking their Herschel SPIRE far-infrared emission.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".