HerMES: CANDIDATE GRAVITATIONALLY LENSED GALAXIES AND LENSING STATISTICS AT SUBMILLIMETER WAVELENGTHS
Bibliographic record
Abstract
We present a list of 13 candidate gravitationally lensed submillimeter galaxies (SMGs) from 95 deg 2 of the Herschel Multi-tiered Extragalactic Survey, a surface density of 0.14 ± 0.04 deg −2 . The selected sources have 500 μm flux densities ( S 500 ) greater than 100 mJy. Gravitational lensing is confirmed by follow-up observations in 9 of the 13 systems (70%), and the lensing status of the four remaining sources is undetermined. We also present a supplementary sample of 29 (0.31 ± 0.06 deg −2 ) gravitationally lensed SMG candidates with S 500 = 80–100 mJy, which are expected to contain a higher fraction of interlopers than the primary candidates. The number counts of the candidate lensed galaxies are consistent with a simple statistical model of the lensing rate, which uses a foreground matter distribution, the intrinsic SMG number counts, and an assumed SMG redshift distribution. The model predicts that 32%–74% of our S 500 ⩾ 100 mJy candidates are strongly gravitationally lensed (μ ⩾ 2), with the brightest sources being the most robust; this is consistent with the observational data. Our statistical model also predicts that, on average, lensed galaxies with S 500 = 100 mJy are magnified by factors of ∼9, with apparently brighter galaxies having progressively higher average magnification, due to the shape of the intrinsic number counts. 65% of the sources are expected to have intrinsic 500 μm flux densities less than 30 mJy. Thus, samples of strongly gravitationally lensed SMGs, such as those presented here, probe below the nominal Herschel detection limit at 500 μm. They are good targets for the detailed study of the physical conditions in distant dusty, star-forming galaxies, due to the lensing magnification, which can lead to spatial resolutions of ∼0 01 in the source plane.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".