JWST’s PEARLS: A JWST/NIRCam View of ALMA Sources
Bibliographic record
Abstract
Abstract We report the results of James Webb Space Telescope/NIRCam observations of 19 (sub)millimeter sources detected by the Atacama Large Millimeter Array (ALMA). The accurate ALMA positions allowed unambiguous identifications of their NIRCam counterparts. Taking gravitational lensing into account, these represent 16 distinct galaxies in three fields and constitute the largest sample of its kind to date. The counterparts’ spectral energy distributions cover from rest-frame ultraviolet to near-IR and provide photometric redshifts (1 < z < 4.5) and stellar masses (M * > 1010.5 M ⊙), which are similar to submillimeter galaxies (SMGs) studied previously. However, our sample is fainter in (sub)millimeter than the classic SMG samples are, and our sources exhibit a wider range of properties. They have dust-embedded star formation rates as low as 10 M ⊙ yr−1, and the sources populate both the star-forming main sequence and the quiescent categories. The deep NIRCam data allow us to study the rest-frame near-IR morphologies. Excluding two multiply imaged systems and one quasar, the majority of the remaining sources are disk-like and show either little or no disturbance. This suggests that secular growth is a potential route for the assembly of high-mass disk galaxies. While a few objects have large disks, the majority have small disks (median half-mass radius of 1.6 kpc). At this time, it is unclear whether this is due to the prevalence of small disks at these redshifts or some unknown selection effects of deep ALMA observations. A larger sample of ALMA sources with NIRCam observations will be able to address this question.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".