ALMACAL VIII: a pilot survey for untargeted extragalactic CO emission lines in deep ALMA calibration data
Bibliographic record
Abstract
ABSTRACT We present a pilot, untargeted extragalactic carbon monoxide (CO) emission-line survey using ALMACAL, a project utilizing ALMA calibration data for scientific purposes. In 33 deep (Texp > 40 min) ALMACAL fields, we report six CO emission-line detections above S/N > 4, one-third confirmed by MUSE observations. With this pilot survey, we probe a cosmologically significant volume of ∼105 cMpc3, widely distributed over many pointings in the southern sky, making the survey largely insusceptible to the effects of cosmic variance. We derive the redshift probability of the CO detections using probability functions from the Shark semi-analytical model of galaxy formation. By assuming typical CO excitations for the detections, we put constraints on the cosmic molecular gas mass density evolution over the redshift range 0 < z < 1.5. The results of our pilot survey are consistent with the findings of other untargeted emission-line surveys and the theoretical model predictions and currently cannot rule out a non-evolving molecular gas mass density. Our study demonstrates the potential of using ALMA calibrator fields as a multi-sightline untargeted CO emission-line survey. Applying this approach to the full ALMACAL database will provide an accurate, free of cosmic variance, measurement of the molecular luminosity function as a function of redshift.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".