On the multiplicity of red- <i>Herschel</i> sources and its implications for extreme star formation
Bibliographic record
Abstract
ABSTRACT We study the multiplicity of galaxies in the largest sample of red-Herschel sources ($S_{250 \, \mu \mathrm{m}} < S_{350 \, \mu \mathrm{m}} < S_{500 \, \mu \mathrm{m}}$) using archival Atacama Large Millimeter/submillimeter Array (ALMA) observations. Out of 2416 fields with ALMA detections (from a total of 3089 analysed maps), we identify 474 multiple systems within a radius of 16 arcsec (equivalent to the 500 $\mu$m Herschel beam size): 420 doubles, 51 triples, and 3 quadruples. In each case, the brightest source contributes, on average, 64, 48, and 42 per cent of the total flux in double, triple, and quadruple systems. The average combined ALMA flux density of the sources in double systems is comparable to that of the two brightest components within triple and quadruple systems. Non-parametric tests suggest that only a small fraction of the double systems (${\lesssim} 13$ per cent) are composed of sources with compatible redshifts, while 47–67 per cent of triple and quadruple fields contain at least one potentially associated pair. Simulations using a mock catalogue of dusty star-forming galaxies suggest that 32 per cent of the double systems are likely physically associated ($\Delta z < 0.01$, i.e. $\lesssim$10 cMpc at $z = 3$) and, while only 8 per cent of the triple and none of the quadruple systems meet this criterion, ${\sim}$70 per cent of them include at least one likely associated pair. Our results suggest that enhanced star formation rates in submillimetre galaxies are primarily driven by internal processes rather than large-scale interactions. This study also provides a catalogue of potential overdensities for follow-up observations, offering insights into protocluster formation and evolution.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".