ALMA 1.3 mm Survey of Lensed Submillimeter Galaxies Selected by Herschel: Discovery of Spatially Extended SMGs and Implications
Bibliographic record
Abstract
Abstract We present an ALMA 1.3 mm (Band 6) continuum survey of lensed submillimeter galaxies (SMGs) at z = 1.0 to ∼3.2 with an angular resolution of ∼0.″2. These galaxies were uncovered by the Herschel Lensing Survey and feature exceptionally bright far-infrared continuum emission (S peak ≳ 90 mJy) owing to their lensing magnification. We detect 29 sources in 20 fields of massive galaxy clusters with ALMA. Using both the Spitzer/IRAC (3.6/4.5 μm) and ALMA data, we have successfully modeled the surface brightness profiles of 26 sources in the rest-frame near- and far-infrared. Similar to previous studies, we find the median dust-to-stellar continuum size ratio to be small (R e,dust/R e,star = 0.38 ± 0.14) for the observed SMGs, indicating that star formation is centrally concentrated. This is, however, not the case for two spatially extended main-sequence SMGs with a low surface brightness at 1.3 mm (≲0.1 mJy arcsec−2), in which the star formation is distributed over the entire galaxy (R e,dust/R e,star > 1). As a whole, our SMG sample shows a tight anticorrelation between (R e,dust/R e,star) and far-infrared surface brightness (ΣIR) over a factor of ≃1000 in ΣIR. This indicates that SMGs with less vigorous star formation (i.e., lower ΣIR) lack central starburst and are likely to retain a broader spatial distribution of star formation over the whole galaxies (i.e., larger R e,dust/R e,star). The same trend can be reproduced with cosmological simulations as a result of central starburst and potentially subsequent “inside-out” quenching, which likely accounts for the emergence of compact quiescent galaxies at z ∼ 2.
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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 | 0.002 | 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) | 0.001 | 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 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".