The SCUBA-2 Cosmology Legacy Survey: demographics of the 450-μm population
Bibliographic record
Abstract
We investigate the multiwavelength properties of a sample of 450-μm-selected sources from the SCUBA-2 Cosmology Legacy Survey. A total of 69 sources were identified above 4σ in deep SCUBA-2 450-μm observations overlapping the UDS and COSMOS fields and covering 210 arcmin to a typical depth of σ = 1.5mJy. Reliable cross-identifications are found for 58 sources (84 per cent) in Spitzer and Hubble Space Telescope WFC3/IR data. The photometric redshift distribution (dN/dz) of 450-μm-selected sources is presented, showing a broad peak in the redshift range 1 <z <3 and a median of z = 1.4. Combining the SCUBA-2 photometry with Herschel SPIRE data from HerMES, the submm spectral energy distribution (SED) is examined via the use of modified blackbody fits, yielding aggregate values for the IR luminosity, dust temperature and emissivity of (L) = 10 L, (TD) = 42 ± 11K and (β) = 1.6 ± 0.5, respectively. The relationship between these SED parameters and the physical properties of galaxies is investigated, revealing correlations between TD and LIR and between βD and both stellar mass and effective radius. The connection between the star formation rate (SFR) and stellar mass is explored, with 24 per cent of 450-μm sources found to be 'starbursts', i.e. displaying anomalously high specific SFRs. However, both the number density and observed properties of these 'starburst' galaxies are found to be consistent with the population of normal star-forming 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".