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 μm 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 degree2 850 μm 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 >4σ 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 1012 . 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 S870 > 7.5 mJy, and by 60% at S870 > 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 × 1012 or a star formation rate (SFR) of ~103 . 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 S870 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".