HerMES: ALMA IMAGING OF<i>HERSCHEL</i>-SELECTED DUSTY STAR-FORMING GALAXIES
Bibliographic record
Abstract
The Herschel Multi-tiered Extragalactic Survey (HerMES) has identified large numbers of dusty star-forming galaxies (DSFGs) over a wide range in redshift. A detailed understanding of these DSFGs is hampered by the limited spatial resolution of Herschel . We present 870 μ m 0 45 resolution imaging obtained with the Atacama Large Millimeter/submillimeter Array (ALMA) of a sample of 29 HerMES DSFGs that have far-infrared (FIR) flux densities that lie between the brightest of sources found by Herschel and fainter DSFGs found via ground-based surveys in the submillimeter region. The ALMA imaging reveals that these DSFGs comprise a total of 62 sources (down to the point-source sensitivity limit in our ALMA sample; ). Optical or near-infrared imaging indicates that 36 of the ALMA sources experience a significant flux boost from gravitational lensing ( ), but only six are strongly lensed and show multiple images. We introduce and make use of uvmcmcfit , a general-purpose and publicly available Markov chain Monte Carlo visibility-plane analysis tool to analyze the source properties. Combined with our previous work on brighter Herschel sources, the lens models presented here tentatively favor intrinsic number counts for DSFGs with a break near at and a steep fall-off at higher flux densities. Nearly 70% of the Herschel sources break down into multiple ALMA counterparts, consistent with previous research indicating that the multiplicity rate is high in bright sources discovered in single-dish submillimeter or FIR surveys. The ALMA counterparts to our Herschel targets are located significantly closer to each other than ALMA counterparts to sources found in the LABOCA ECDFS Submillimeter Survey. Theoretical models underpredict the excess number of sources with small separations seen in our ALMA sample. The high multiplicity rate and small projected separations between sources seen in our sample argue in favor of interactions and mergers plausibly driving both the prodigious emission from the brightest DSFGs as well as the sharp downturn above .
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".