HerMES: CANDIDATE GRAVITATIONALLY LENSED GALAXIES AND LENSING STATISTICS AT SUBMILLIMETER WAVELENGTHS
Bibliographic record
Abstract
Gravitational lensing increases the angular size and integrated flux of affected sources. It is exploited to investigate the mass distribution of the foreground lensing structures and the properties of the background lensed galaxies (see reviews by Bartelmann 2010; Treu 2010). \n \nThe magnification provided by gravitational lensing makes it an effective tool for identifying and studying intrinsically faint and typically distant galaxies (e.g., Stark et al. 2007; Richard et al. 2008, 2011). The flux boost from lensing yields an improved detection, and the associated spatial enhancement increases the ability to investigate the internal structure of distant galaxies to levels otherwise unattainable with the current generation of instrumentation (e.g., Riechers et al. 2008; Swinbank et al. 2010, 2011; Gladders et al. 2012). Furthermore, gravitational lensing probes the total mass of the foreground deflectors, including the relative content of dark and luminous mass. In combination with dynamical studies, lensing mass reconstruction allows one to obtain the density profile of the dark matter in individual lensing galaxies down to ~10 kpc scales (e.g., Miralda-Escude 1995; Dalal & Kochanek 2002; Metcalf & Zhao 2002; Rusin & Kochanek 2005; Treu & Koopmans 2004).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.010 | 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".