The SCUBA Half-Degree Extragalactic Survey (SHADES) – VIII. The nature of faint submillimetre galaxies in SHADES, SWIRE and SXDF surveys
Bibliographic record
Abstract
We present the optical-to-submillimetre spectral energy distributions (SEDs) for 33 radio and mid-infrared (mid-IR) identified submillimetre galaxies discovered via the SHADES 850-?m SCUBA imaging in the Subaru-XMM Deep Field (SXDF). Optical data for the sources come from the SXDF and mid- and far-IR fluxes from SWIRE. We obtain photometric redshift estimates for our sources using optical and IRAC 3.6- and 4.5-?m fluxes. We then fit SED templates to the longer wavelength data to determine the nature of the far-IR emission that dominates the bolometric luminosity of these sources. The IR template fits are also used to resolve ambiguous identifications and cases of redshift aliasing. The redshift distribution obtained broadly matches previous results for submillimetre sources and on the SHADES SXDF field. Our template fitting finds that active galactic nuclei, while present in about 10 per cent of our sources, do not contribute significantly to their bolometric luminosity. Dust heating by starbursts, with either Arp220 or M82 type SEDs, appears to be responsible for the luminosity in most sources (23/33 are fitted by Arp220 templates, 2/33 by the warmer M82 templates). 8/33 sources, in contrast, are fitted by a cooler cirrus dust template, suggesting that cold dust has a role in some of these highly luminous objects. Three of our sources appear to have multiple identifications or components at the same redshift, but we find no statistical evidence that close associations are common among our SHADES sources. Examination of rest-frame K-band luminosity suggests that 'downsizing' is underway in the submillimetre galaxy population, with lower redshift systems lying in lower mass host galaxies. Of our 33 identifications six are found to be of lower reliability but their exclusion would not significantly alter our conclusions.
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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 | 0.002 | 0.001 |
| 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".