SCUBA-2 Ultra Deep Imaging EAO Survey (STUDIES): Faint-end Counts at 450 μm
Bibliographic record
Abstract
Abstract The SCUBA-2 Ultra Deep Imaging EAO Survey (STUDIES) is a three-year JCMT Large Program aiming to reach the 450 μm confusion limit in the COSMOS-CANDELS region to study a representative sample of the high-redshift far-infrared galaxy population that gives rise to the bulk of the far-infrared background. We present the first-year data from STUDIES. We reached a 450 μm noise level of 0.91 mJy for point sources at the map center, covered an area of 151 arcmin2, and detected 98 and 141 sources at 4.0σ and 3.5σ, respectively. Our derived counts are best constrained in the 3.5–25 mJy regime using directly detected sources. Below the detection limits, our fluctuation analysis further constrains the slope of the counts down to 1 mJy. The resulting counts at 1–25 mJy are consistent with a power law having a slope of −2.59 (±0.10 for 3.5–25 mJy, and − 0.7 + 0.4 for 1–3.5 mJy). There is no evidence of a faint-end termination or turnover of the counts in this flux density range. Our counts are also consistent with previous SCUBA-2 blank-field and lensing-cluster surveys. The integrated surface brightness from our counts down to 1 mJy is 90.0 ± 17.2 Jy deg−2, which can account for up to 83 − 16 + 15 % of the COBE 450 μm background. We show that Herschel counts at 350 and 500 μm are significantly higher than our 450 μm counts, likely caused by its large beam and source clustering. High angular resolution instruments like SCUBA-2 at 450 μm are therefore highly beneficial for measuring the luminosity and spatial density of high-redshift dusty 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".