moka: a new tool for strong lensing studies
Bibliographic record
Abstract
Strong gravitational lensing is a powerful tool that can be used to probe the matter distribution in the cores of massive dark matter haloes. Recent and ongoing analyses of galaxy cluster surveys – such as the Massive Cluster Survey (MACS), the Canada–France–Hawaii Telescope Legacy Survey (CFHTLS), the Sloan Digital Sky Survey (SDSS), the Sloan Giant Arcs Survey (SGAS), the Cluster Lensing and Supernova Survey with Hubble (CLASH) and the Local Cluster Substructure Survey (LoCuSS) – have addressed the question of the nature of the dark matter distribution in clusters. Using N‐body simulations of cold dark matter haloes, it is consistently found that haloes should be characterized by a concentration–mass relation, which decreases monotonically with halo mass, and that they should be populated by a large amount of substructures, representing the cores of accreted progenitor halos. It is important for our understanding of dark matter that we test these predictions. We present moka, a new algorithm for simulating the gravitational lensing signal from cluster‐sized haloes. It implements the most recent results from numerical simulations to create realistic cluster‐scale lenses with properties independent of numerical resolution. We perform systematic studies of the strong lensing cross‐section as a function of halo structures. We find that the strong lensing cross‐sections depend most strongly on the concentration and on the inner slope of the density profile of a halo, followed in order of importance by halo triaxiality and the presence of a bright central galaxy.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".