A deep ALMA image of the<i>Hubble Ultra Deep Field</i>
Bibliographic record
Abstract
We present the results of the first, deep Atacama Large Millimeter Array (ALMA) imaging covering the full ≃4.5 arcmin2 of the Hubble Ultra Deep Field (HUDF) imaged with Wide Field Camera 3/IR on HST. Using a 45-pointing mosaic, we have obtained a homogeneous 1.3-mm image reaching σ1.3 ≃ 35 μJy, at a resolution of ≃0.7 arcsec. From an initial list of ≃50 > 3.5σ peaks, a rigorous analysis confirms 16 sources with S1.3 > 120 μJy. All of these have secure galaxy counterparts with robust redshifts (〈z〉 = 2.15). Due to the unparalleled supporting data, the physical properties of the ALMA sources are well constrained, including their stellar masses (M*) and UV+FIR star formation rates (SFR). Our results show that stellar mass is the best predictor of SFR in the high-redshift Universe; indeed at z ≥ 2 our ALMA sample contains seven of the nine galaxies in the HUDF with M* ≥ 2 × 1010 M⊙, and we detect only one galaxy at z > 3.5, reflecting the rapid drop-off of high-mass galaxies with increasing redshift. The detections, coupled with stacking, allow us to probe the redshift/mass distribution of the 1.3-mm background down to S1.3 ≃ 10 μJy. We find strong evidence for a steep star-forming ‘main sequence’ at z ≃ 2, with SFR ∝M* and a mean specific SFR ≃ 2.2 Gyr−1. Moreover, we find that ≃85 per cent of total star formation at z ≃ 2 is enshrouded in dust, with ≃65 per cent of all star formation at this epoch occurring in high-mass galaxies (M* > 2 × 1010 M⊙), for which the average obscured:unobscured SF ratio is ≃200. Finally, we revisit the cosmic evolution of SFR density; we find this peaks at z ≃ 2.5, and that the star-forming Universe transits from primarily unobscured to primarily obscured at z ≃ 4.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".