An ALMA survey of the S2CLS UDS field: optically invisible submillimetre galaxies
Bibliographic record
Abstract
ABSTRACT We analyse a robust sample of 30 near-infrared-faint (KAB > 25.3, 5σ) submillimetre galaxies (SMGs) selected from a 0.96 deg2 field to investigate their properties and the cause of their faintness in optical/near-infrared wavebands. Our analysis exploits precise identifications based on Atacama Large Millimeter Array (ALMA) 870-μm continuum imaging, combined with very deep near-infrared imaging from the UKIDSS Ultra Deep Survey. We estimate that SMGs with KAB > 25.3 mag represent 15 ± 2 per cent of the total population brighter than S870 = 3.6 mJy, with a potential surface density of ∼450 deg−2 above S870 ≥ 1 mJy. As such, they pose a source of contamination in surveys for both high-redshift ‘quiescent’ galaxies and very high redshift Lyman-break galaxies. We show that these K-faint SMGs represent the tail of the broader submillimetre population, with comparable dust and stellar masses to KAB ≤ 25.3 mag SMGs, but lying at significantly higher redshifts (z = 3.44 ± 0.06 versus z = 2.36 ± 0.11) and having higher dust attenuation (AV = 5.2 ± 0.3 versus AV = 2.9 ± 0.1). We investigate the origin of the strong dust attenuation and find indications that these K-faint galaxies have smaller dust continuum sizes than the KAB ≤ 25.3 mag galaxies, as measured by ALMA, which suggests their high attenuation is related to their compact sizes. We identify a correlation of dust attenuation with star formation rate surface density (ΣSFR), with the K-faint SMGs representing the higher ΣSFR and highest AV galaxies. The concentrated, intense star formation activity in these systems is likely to be associated with the formation of spheroids in compact galaxies at high redshifts, but as a result of their high obscuration these galaxies are completely missed in ultraviolet, optical, and even near-infrared surveys.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".