LENS MODELS OF<i>HERSCHEL</i>-SELECTED GALAXIES FROM HIGH-RESOLUTION NEAR-IR OBSERVATIONS
Bibliographic record
Abstract
We present Keck-Adaptive Optics and Hubble Space Telescope high resolution near-infrared (IR) imaging for 500 μm bright candidate lensing systems identified by the Herschel Multi-tiered Extragalactic Survey and Herschel Astrophysical Terahertz Large Area Survey. Out of 87 candidates with near-IR imaging, 15 (∼17%) display clear near-IR lensing morphologies. We present near-IR lens models to reconstruct and recover basic rest-frame optical morphological properties of the background galaxies from 12 new systems. Sources with the largest near-IR magnification factors also tend to be the most compact, consistent with the size bias predicted from simulations and previous lensing models for submillimeter galaxies (SMGs). For four new sources that also have high-resolution submillimeter maps, we test for differential lensing between the stellar and dust components and find that the 880 μm magnification factor (μ 880 ) is ∼1.5 times higher than the near-IR magnification factor (μ NIR ), on average. We also find that the stellar emission is ∼2 times more extended in size than dust. The rest-frame optical properties of our sample of Herschel -selected lensed SMGs are consistent with those of unlensed SMGs, which suggests that the two populations are similar.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".