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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".