CANDIDATE GRAVITATIONALLY LENSED DUSTY STAR-FORMING GALAXIES IN THE HERSCHEL WIDE AREA SURVEYS*
Bibliographic record
Abstract
ABSTRACT We present a list of candidate gravitationally lensed dusty star-forming galaxies (DSFGs) from the HerMES Large Mode Survey and the Herschel Stripe 82 Survey. Together, these partially overlapping surveys cover 372 deg2 on the sky. After removing local spiral galaxies and known radio-loud blazars, our candidate list of lensed DSFGs is composed of 77 sources with 500 μm flux densities (S 500) greater than 100 mJy. Such sources are dusty starburst galaxies similar to the first bright sub-millimeter galaxies (SMGs) discovered with SCUBA. We expect a large fraction of this list to be strongly lensed, with a small fraction made up of bright SMG–SMG mergers that appear as hyper-luminous infrared galaxies ( L IR > 10 13 L ⊙ ). Thirteen of the 77 candidates have spectroscopic redshifts from CO spectroscopy with ground-based interferometers, putting them at z > 1 and well above the redshift of the foreground lensing galaxies. The surface density of our sample is 0.21 ± 0.03 deg−2. We present follow-up imaging of a few of the candidates to confirm their lensing nature. The sample presented here is an ideal tool for higher-resolution imaging and spectroscopic observations to understand the detailed properties of starburst phenomena in distant galaxies.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".