ALMA twenty-six arcmin2 survey of GOODS-S at one millimeter (ASAGAO): Millimeter properties of stellar mass selected galaxies
Bibliographic record
Abstract
Abstract We make use of the ALMA twenty-Six Arcmin2 survey of GOODS-S At One-millimeter (ASAGAO), deep 1.2 mm continuum observations of a 26-arcmin2 region in the Great Observatories Origins Deep Survey-South (GOODS-S) obtained with Atacama Large Millimeter/sub-millimeter Array (ALMA), to probe dust-enshrouded star formation in K-band selected (i.e., stellar mass selected) galaxies, which are drawn from the FourStar Galaxy Evolution Survey (ZFOURGE) catalog. Based on the ASAGAO combined map, which was created by combining ASAGAO and ALMA archival data in the GOODS-South field, we find that 24 ZFOURGE sources have 1.2 mm counterparts with a signal-to-noise ratio >4.5 (1σ ≃ 30–70 μJy beam−1 at 1.2 mm). Their median redshift is estimated to be $z$median = 2.38 ± 0.14. They generally follow the tight relationship of the stellar mass versus star formation rate (i.e., the main sequence of star-forming galaxies). ALMA-detected ZFOURGE sources exhibit systematically larger infrared (IR) excess (IRX ≡ LIR/LUV) compared to ZFOURGE galaxies without ALMA detections even though they have similar redshifts, stellar masses, and star formation rates. This implies the consensus stellar-mass versus IRX relation, which is known to be tight among rest-frame-ultraviolet-selected galaxies, cannot fully predict the ALMA detectability of stellar-mass-selected galaxies. We find that ALMA-detected ZFOURGE sources are the main contributors to the cosmic IR star formation rate density at $z$ = 2–3.
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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 | 0.002 |
| 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".