Mapping Obscuration to Reionization with ALMA (MORA): 2 mm Efficiently Selects the Highest-redshift Obscured Galaxies
Bibliographic record
Abstract
Abstract We present the characteristics of 2 mm selected sources from the largest Atacama Large Millimeter/submillimeter Array (ALMA) blank-field contiguous survey conducted to date, the Mapping Obscuration to Reionization with ALMA (MORA) survey covering 184 arcmin2 at 2 mm. Twelve of 13 detections above 5σ are attributed to emission from galaxies, 11 of which are dominated by cold dust emission. These sources have a median redshift of 〈 z 2 mm 〉 = 3.6 − 0.3 + 0.4 primarily based on optical/near-infrared photometric redshifts with some spectroscopic redshifts, with 77% ± 11% of sources at z > 3 and 38% ± 12% of sources at z > 4. This implies that 2 mm selection is an efficient method for identifying the highest-redshift dusty star-forming galaxies (DSFGs). Lower-redshift DSFGs (z < 3) are far more numerous than those at z > 3 yet are likely to drop out at 2 mm. MORA shows that DSFGs with star formation rates in excess of 300 M ⊙ yr−1 and a relative rarity of ∼10−5 Mpc−3 contribute ∼30% to the integrated star formation rate density at 3 < z < 6. The volume density of 2 mm selected DSFGs is consistent with predictions from some cosmological simulations and is similar to the volume density of their hypothesized descendants: massive, quiescent galaxies at z > 2. Analysis of MORA sources’ spectral energy distributions hint at steeper empirically measured dust emissivity indices than reported in typical literature studies, with 〈 β 〉 = 2.2 − 0.4 + 0.5 . The MORA survey represents an important step in taking census of obscured star formation in the universe’s first few billion years, but larger area 2 mm surveys are needed to more fully characterize this rare population and push to the detection of the universe’s first dusty galaxies.
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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 | 0.001 | 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) | 0.003 | 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".