THE SCUBA-2 COSMOLOGY LEGACY SURVEY: ALMA RESOLVES THE BRIGHT-END OF THE SUB-MILLIMETER NUMBER COUNTS
Bibliographic record
Abstract
We present high-resolution 870 μ m Atacama Large Millimeter/sub-millimeter Array (ALMA) continuum maps of 30 bright sub-millimeter sources in the UKIDSS UDS field. These sources are selected from deep, 1 degree 2 850 μ m maps from the SCUBA-2 Cosmology Legacy Survey, and are representative of the brightest sources in the field (median = 8.7 ± 0.4 mJy). We detect 52 sub-millimeter galaxies (SMGs) at >4 σ significance in our 30 ALMA maps. In of the ALMA maps the single-dish source comprises a blend of ≥2 SMGs, where the secondary SMGs are Ultra-luminous Infrared Galaxies (ULIRGs) with 10 12 . The brightest SMG contributes on average of the single-dish flux density, and in the ALMA maps containing ≥2 SMGs the secondary SMG contributes of the integrated ALMA flux. We construct source counts and show that multiplicity boosts the apparent single-dish cumulative counts by 20% at S 870 > 7.5 mJy, and by 60% at S 870 > 12 mJy. We combine our sample with previous ALMA studies of fainter SMGs and show that the counts are well-described by a double power law with a break at 8.5 ± 0.6 mJy. The break corresponds to a luminosity of ∼6 × 10 12 or a star formation rate (SFR) of ∼10 3 . For the typical sizes of these SMGs, which are resolved in our ALMA data with = 1.2 ± 0.1 kpc, this yields a limiting SFR density of ∼100 yr −1 kpc −2 Finally, the number density of S 870 2 mJy SMGs is 80 ± 30 times higher than that derived from blank-field counts. An over-abundance of faint SMGs is inconsistent with line-of-sight projections dominating multiplicity in the brightest SMGs, and indicates that a significant proportion of these high-redshift ULIRGs are likely to be physically associated.
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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 | 0.003 | 0.002 |
| 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".