ALMA twenty-six arcmin2 survey of GOODS-S at one millimeter (ASAGAO): Source catalog and number counts
Bibliographic record
Abstract
Abstract We present the survey design, data reduction, construction of images, and source catalog of the Atacama Large Millimeter/submillimeter Array (ALMA) twenty-six arcmin2 survey of GOODS-S at one millimeter (ASAGAO). ASAGAO is a deep (1 σ depth ∼ 61 μJy beam−1 for a 250 kλ-tapered map with a synthesized beam size of 0${^{\prime\prime}_{.}}$51 × 0${^{\prime\prime}_{.}}$45) and wide area (26 arcmin2) survey on a contiguous field at 1.2 mm. By combining with ALMA archival data in the GOODS-South field, we obtained a deeper map in the same region (1 σ depth ∼ 30 μJy beam−1 for a deep region with a 250 kλ-taper, and a synthesized beam size of 0${^{\prime\prime}_{.}}$59 × 0${^{\prime\prime}_{.}}$53), providing the largest sample of sources (25 sources at ≥5.0 σ, 45 sources at ≥4.5 σ) among ALMA blank-field surveys to date. The number counts show that $52^{+11}_{-8}$% of the extragalactic background light at 1.2 mm is resolved into discrete sources at S1.2 mm > 135 μJy. We create infrared (IR) luminosity functions (LFs) in the redshift range of $z$ = 1–3 from the ASAGAO sources with Ks-band counterparts, and constrain the faintest luminosity of the LF at 2.0 < $z$ < 3.0. The LFs are consistent with previous results based on other ALMA and SCUBA-2 observations, which suggest a positive luminosity evolution and negative density evolution with increasing redshift. We find that obscured star-formation of sources with IR luminosities of log (LIR/L⊙) ≳ 11.8 account for ≈60%–90% of the $z$ ∼ 2 cosmic star-formation rate density.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".