The SCUBA-2 Large eXtragalactic Survey: 850μm map, catalogue and the bright-end number counts of the<i>XMM-</i>LSS field
Bibliographic record
Abstract
ABSTRACT We present $850\, \mu {\rm m}$ imaging of the XMM-LSS field observed for 170 h as part of the James Clerk Maxwell Telescope SCUBA-2 Large eXtragalactic Survey (S2LXS). S2LXS XMM-LSS maps an area of $9\, {\rm deg}^2$, reaching a moderate depth of $1\sigma \simeq 4\, {\rm mJy\, beam^{-1}}$. This is the largest contiguous area of extragalactic sky mapped by James Clerk Maxwell Telescope (JCMT) at $850\, \mu {\rm m}$ to date. The wide area of the S2LXS XMM-LSS survey allows us to probe the ultra-bright ($S_{\rm 850\mu m}\gtrsim 15\, {\rm mJy}$), yet rare submillimetre population. We present the S2LXS XMM-LSS catalogue, which comprises 40 sources detected at >5σ significance, with deboosted flux densities in the range of $7$–$48\, {\rm mJy}$. We robustly measure the bright-end of the $850\, \mu {\rm m}$ number counts at flux densities ${\gt }7\, {\rm mJy}$, reducing the Poisson errors compared to existing measurements. The S2LXS XMM-LSS observed number counts show the characteristic upturn at bright fluxes, expected to be motivated by local sources of submillimetre emission and high-redshift strongly lensed galaxies. We find that the observed $850\, \mu {\rm m}$ number counts are best reproduced by model predictions that include either strong lensing or source blending from a 15-arcsec beam, indicating that both may make an important contribution to the observed overabundance of bright single dish $850\, \mu {\rm m}$ selected sources. We make the S2LXS XMM-LSS $850\, \mu {\rm m}$ map and >5σ catalogue presented here publicly available.
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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 | 0.004 | 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) | 0.004 | 0.003 |
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".