Multiband Optical and Near-Infrared Properties of Faint Submillimeter Galaxies with Serendipitous ALMA Detections
Bibliographic record
Abstract
Abstract We present a catalog of 26 faint submillimeter galaxies (SMGs) in the XMM Large Scale Structure (XMM-LSS) field identified by cross-matching serendipitously detected sources in archival pre–Atacama Large Millimeter Array (ALMA) band 6 and 7 data with multiband near-infrared (NIR) and optical data from the Spitzer Extragalactic Representative Volume Survey, the VISTA Deep Extragalactic Survey, the Canada–France–Hawaii Telescope Legacy Large Survey, and the Hyper Suprime-Cam Subaru Strategic Program. Of the 26 SMGs in our sample, 15 are identified here for the first time. The majority of the sources in our sample (16/26) have faint submillimeter fluxes (0.1 mJy < S 1mm < 1 mJy). In addition to the 26 SMGs with multiband optical and NIR detections, there are 60 highly reliable (>5σ) ALMA sources with no counterpart in any other band down to an Infrared Array Camera (IRAC) [4.5] AB magnitude of ≈23.7. To further characterize the 26 galaxies with both ALMA and optical/NIR counterparts, we provide 13-band forced photometry for the entire catalog using the Tractor and calculate photometric redshifts and rest-frame colors. The median redshift of our sample is . We find that our sample galaxies have bluer colors compared to bright SMGs, and the UVJ color plot indicates that their colors are consistent with main-sequence star-forming galaxies. Our results provide new insights into the nature of the faint population of SMGs and also highlight opportunities for galaxy evolution studies based on archival ALMA data.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".