A<sup>3</sup>COSMOS and A<sup>3</sup>GOODSS: Continuum source catalogues and multi-band number counts
Bibliographic record
Abstract
Context. Galaxy submillimetre number counts are a fundamental measurement in our understanding of galaxy evolution models. Most early measurements are obtained via single-dish telescopes with substantial source confusion, whereas recent interferometric observations are limited to small areas. Aims. We used a large database of ALMA continuum observations to accurately measure galaxy number counts in multiple (sub)millimetre bands, thus bridging the flux density range between single-dish surveys and deep interferometric studies. Methods. We continued the Automated Mining of the ALMA Archive in the COSMOS Field project (A3COSMOS) and extended it with observations from the GOODS-South field (A3GOODSS). The database consists of ∼4000 pipeline-processed continuum images from the public ALMA archive, yielding 2050 unique detected sources, including sources with and without a known optical counterpart. To infer galaxy number counts, we constructed a method to reduce the observational bias inherent to targeted pointings that dominate the database. This method comprises a combination of image selection, masking, and source weighting. The effective area was calculated by accounting for inhomogeneous wavelengths, sensitivities, and resolutions and for the spatial overlap between images. We tested and calibrated our method with simulations. Results. We derived the number counts in a consistent and homogeneous way in four different ALMA bands covering a relatively large area. The results are consistent with number counts retrieved from the literature within the uncertainties. In Band 7, at the depth of the inferred number counts, ∼40% of the cosmic infrared background is resolved into discrete sources. This fraction, however, decreases with increasing wavelength, reaching ∼4% in Band 3. Finally, we used the number counts to test models of dusty galaxy evolution, and find a good agreement within the uncertainties. Conclusions. By continuing the A3COSMOS and A3GOODSS archival effort, we obtained the deepest archive-based (sub)millimetre number counts measured to date over such a wide area. This database proves to be a valuable resource that, thanks to its substantial size, can be used for statistical analyses after having applied certain conservative restrictions.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
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".