Observational constraints on the physical nature of submillimetre source multiplicity: chance projections are common
Bibliographic record
Abstract
Interferometric observations have demonstrated that a significant fraction of single-dish submillimetre (submm) sources are blends of multiple submm galaxies (SMGs), but the nature of this multiplicity, i.e. whether the galaxies are physically associated or chance projections, has not been determined. We performed spectroscopy of 11 SMGs in six multicomponent submm sources, obtaining spectroscopic redshifts for nine of them. For an additional two component SMGs, we detected continuum emission but no obvious features. We supplement our observed sources with four single-dish submm sources from the literature. This sample allows us to statistically constrain the physical nature of single-dish submm source multiplicity for the first time. In three |$(3/7, \rm{ or}\,\,43^{+39 }_{ -33}\,\,\rm{per\,\,cent\,\,\,\,at\,\,95\,\,{per\,\,cent}\,\,confidence})$| of the single-dish sources for which the nature of the blending is unambiguous, the components for which spectroscopic redshifts are available are physically associated, whereas 4/7 |$(57^{+33 }_{ -39} \text{ per cent})$| have at least one unassociated component. When components whose spectra exhibit continuum but no features and for which the photometric redshift is significantly different from the spectroscopic redshift of the other component are also considered, 6/9 |$(67^{+26 }_{ -37} \text{ per cent})$| of the single-dish sources are comprised of at least one unassociated component SMG. The nature of the multiplicity of one single-dish source is ambiguous. We conclude that physically associated systems and chance projections both contribute to the multicomponent single-dish submm source population. This result contradicts the conventional wisdom that bright submm sources are solely a result of merger-induced starbursts, as blending of unassociated galaxies is also important.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".