Caustics: A Python Package for Accelerated StrongGravitational Lensing Simulations
Bibliographic record
Abstract
Gravitational lensing is the deflection of light rays due to the gravity of intervening masses.This phenomenon is observed at a variety of configurations, involving any non-uniform mass such as planets, stars, galaxies, clusters of galaxies, and even the large-scale structure of the Universe.Strong lensing occurs when the distortions are significant and multiple images of the background source are observed.The lens and lensed object(s) must be aligned closely on the sky.As the discovery of lens systems has grown to the low thousands, these systems have become pivotal for precision measurements in astrophysics, notably for phenomena including dark matter (e.g.Hezaveh et al., 2016;Vegetti & Vogelsberger, 2014), supernovae (e.g.Rodney et al., 2021), quasars (e.g.Peng et al., 2006), the first stars (e.g.Welch et al., 2022), and the Universe's expansion rate (e.g.K. C. Wong et al., 2020).With future surveys expected to discover hundreds of thousands of lensing systems, the modelling and simulation of such systems must be done at orders of magnitude larger scale than ever before.Here we present caustics, a Python package designed to facilitate machine learning and Bayesian methods to handle the extensive computational demands of modelling such a vast number of lensing systems.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.040 | 0.013 |
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".