ALMA Lensing Cluster Survey: ALMA-Herschel Joint Study of Lensed Dusty Star-forming Galaxies across z ≃ 0.5 – 6
Bibliographic record
Abstract
Abstract We present an ALMA-Herschel joint analysis of sources detected by the ALMA Lensing Cluster Survey (ALCS) at 1.15 mm. Herschel/PACS and SPIRE data at 100–500 μm are deblended for 180 ALMA sources in 33 lensing cluster fields that are detected either securely (141 sources; in our main sample) or tentatively at S/N ≥ 4 with cross-matched HST/Spitzer counterparts, down to a delensed 1.15 mm flux density of ∼0.02 mJy. We performed far-infrared spectral energy distribution modeling and derived the physical properties of dusty star formation for 125 sources (109 independently) that are detected at >2σ in at least one Herschel band. A total of 27 secure ALCS sources are not detected in any Herschel bands, including 17 optical/near-IR-dark sources that likely reside at z = 4.2 ± 1.2. The 16th, 50th, and 84th percentiles of the redshift distribution are 1.15, 2.08, and 3.59, respectively, for ALCS sources in the main sample, suggesting an increasing fraction of z ≃ 1 − 2 galaxies among fainter millimeter sources (f 1150 ∼ 0.1 mJy). With a median lensing magnification factor of μ = 2.6 − 0.8 + 2.6 , ALCS sources in the main sample exhibit a median intrinsic star formation rate of 94 − 54 + 84 M ⊙ yr−1, lower than that of conventional submillimeter galaxies at similar redshifts by a factor of ∼3. Our study suggests weak or no redshift evolution of dust temperature with L IR < 1012 L ⊙ galaxies within our sample at z ≃ 0 − 2. At L IR > 1012 L ⊙, the dust temperatures show no evolution across z ≃ 1–4 while being lower than those in the local universe. For the highest-redshift source in our sample (z = 6.07), we can rule out an extreme dust temperature (>80 K) that was reported for MACS0416 Y1 at z = 8.31.
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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 | 0.003 | 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 | 0.000 |
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 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".