The Hubble Deep Field North SCUBA Super-map III - Optical and near-infrared properties of submillimetre galaxies
Bibliographic record
Abstract
We present a new sub-mm Super-map in the HDF-North region (GOODS-North field), containing 40 statistically robust sources at 850 microns. This map contains additional data, and several new sources, including one of the brightest blank-sky extragalactic sub-mm sources ever detected. We have used the ACS HST images and ground-based near-IR observations from GOODS to develop a systematic approach for counterpart identification. 72 per cent of our sources with optical coverage have a unique optical counterpart using our new techniques for counterpart identification, and an additional 18 per cent have more than one possibility that meet our criteria in the ACS images. We have found a much higher ERO rate than other sub-mm surveys, due to the increased depth in the optical images. The median photometric redshift (and quartile range), from optical and near-infrared data, is 1.7 (1.3-2.5) for the radio-detected sub-mm sources, and rises to 2.3 (1.3-2.7) for the radio-undetected sub-sample. We find interesting correlations between the 850 micron flux and both the i magnitude and the photometric redshift, from which there appears to be an absence of high redshift faint counterparts to the lower flux density SCUBA sources. While the quantitative morphologies span a range of values, in general the sub-mm galaxies show larger sizes and a higher degree of asymmetry than other galaxy populations at the same redshifts.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".