The SCUBA HAlf Degree Extragalactic Survey - III. Identification of radio and mid-infrared counterparts to submillimetre galaxies
Bibliographic record
Abstract
Determining an accurate position for a submm galaxy (SMG) is the crucial step that enables us to move from the basic properties of an SMG sample - source counts and 2-D clustering - to an assessment of their detailed, multi-wavelength properties, their contribution to the history of cosmic star formation and their links with present-day galaxy populations. In this paper, we identify robust radio and/or IR counterparts, and hence accurate positions, for over two thirds of the SCUBA HAlf-Degree Extragalactic Survey (SHADES) Source Catalogue, presenting optical, 24-um and radio images of each SMG. Observed trends in identification rate have given no strong rationale for pruning the sample. Uncertainties in submm position are found to be consistent with theoretical expectations, with no evidence for significant additional sources of error. Employing the submm/radio redshift indicator, via a parameterisation appropriate for radio-identified SMGs with spectroscopic redshifts, yields a median redshift of 2.8 for the radio-identified subset of SHADES, somewhat higher than the median spectroscopic redshift. We present a diagnostic colour-colour plot, exploiting Spitzer photometry, in which we identify regions commensurate with SMGs at very high redshift. Finally, we find that significantly more SMGs have multiple robust counterparts than would be expected by chance, indicative of physical associations. These multiple systems are most common amongst the brightest SMGs and are typically separated by 2-6", or 15-50/(sin i) kpc at z ~ 2, consistent with early bursts seen in merger simulations.
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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 | 0.004 | 0.003 |
| 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) | 0.005 | 0.002 |
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".