EasyCritics – I. Efficient detection of strongly lensing galaxy groups and clusters in wide-field surveys
Bibliographic record
Abstract
We present EasyCritics, an algorithm to detect strongly lensing groups and clusters in wide-field surveys without relying on a direct recognition of arcs. EasyCritics assumes that light traces mass in order to predict the most likely locations of critical curves from the observed fluxes of luminous red early-type galaxies in the line of sight. The positions, redshifts, and fluxes of these galaxies constrain the idealized gravitational lensing potential as a function of source redshift up to five free parameters, which are calibrated on few known lenses. From the lensing potential, EasyCritics derives the critical curves for a given, representative source redshift. The code is highly parallelized, uses fast Fourier methods and, optionally, GPU acceleration in order to process large data sets efficiently. The search of a |$\smash{1 \, \mathrm{deg}^2}$| field of view requires less than 1 min on a modern quad-core CPU, when using a pixel resolution of 0.25 arcsec px−1. In this first part of a paper series on EasyCritics, we describe the main underlying concepts and present a first demonstration on data from the Canada–France–Hawaii-Telescope Lensing Survey. We show that EasyCritics is able to identify known group- and cluster-scale lenses, including a cluster with two giant arc candidates that were previously missed by automated arc detectors.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".