An optimal ALMA image of the Hubble Ultra Deep Field in the era of <i>JWST</i>: obscured star formation and the cosmic far-infrared background
Bibliographic record
Abstract
ABSTRACT We combine archival ALMA data targeting the Hubble Ultra Deep Field (HUDF) to produce the deepest currently attainable 1-mm maps of this key region. Our deepest map covers 4.2 arcmin2, with a beamsize of 1.49 arcsec $\, {\times }\, 1.07\,$ arcsec at an effective frequency of 243 GHz (1.23 mm). It reaches an rms of 4.6 μJy beam$^{-1}$, with 1.5 arcmin2 below 9.0 μJy beam−1, an improvement of ${\gt }\,$5 per cent (and up to 50 per cent in some regions) over the best previous map. We also make a wider, shallower map, covering 25.4 arcmin2. We detect 45 galaxies in the deep map down to 3.6$\sigma$, 10 more than previously detected, and 39 of these galaxies have JWST counterparts. A stacking analysis on the positions of ALMA-undetected JWST galaxies yields 10 per cent more signal compared to previous stacking analyses, and we find that detected sources plus stacking contribute (10.0 ${\pm }$ 0.5) Jy deg−2 to the cosmic infrared background (CIB) at 1.23 mm. Although this is short of the (uncertain) background level of about 20 Jy deg−2, we show that our measurement is consistent with the background if the HUDF is a mild (${\sim }\, 2\sigma$) negative CIB fluctuation, and that the contribution from faint undetected objects is small and converging. This suggests that JWST has detected essentially all of the galaxies that contribute to the CIB, as anticipated from the strong correlation between galaxy stellar mass and obscured star formation.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".