Dusty star forming galaxies at high-redshift: The Canada-UK deep submillimeter survey [Abstract]
Bibliographic record
Abstract
We present the complete submillimeter data for the Canada-UK Deep Submillimeter Survey with SCUBA on the JCMT. We have imaged two fields, covering approximately 100 square arcminutes and have assembled a sample of 50 objects with S850um > 3 mJy. The objects discovered in this work, and through other surveys with SCUBA, are broadly consistent in spectral energy distribution with the local ultra-luminous infrared galaxies but are much more common at high-redshift. They are forming stars at unprecedented rates of ~1000 Mo/yr. We discuss the source counts and angular clustering properties of these objects in the context of galaxy formation. We have a marginal detection of strong clustering, which suggests these objects formed in the most massive dark halos of the early universe. We summarize the current identifications and redshifts of these galaxies, determined through optical, near-IR, far-IR, radio and 450um data. We find ~10% of the objects lie at z < 1 and estimate a mean redshift for the population of z ~2. In line with other surveys, we are finding a small but significant fraction of sources are identified with Extremely Red Objects and Lyman-break 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".