Polar coordinate transformations for machine learning based dark matter subhalo detection in strong gravitational lenses

arXiv:2607.02663 · astro-ph.GA, astro-ph.CO · Submitted 2026-07-02 · Read on arXiv

Jacob M Campbell, Simon Dye, Emma Chapman, Adam Moss

astro-ph.GA, astro-ph.CO

Submitted: 2026-07-02

Comments: 10 pages, 9 figures

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Loss Function — 8× in this paper · explained in 2 episodes
• Gravitational Lensing — 4× in this paper · explained in 2 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Abundance — 2× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Data Augmentation — 2× in this paper · explained in 7 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Aleatoric Uncertainty — 1× in this paper · explained in 4 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Negative Log-Likelihood — 1× in this paper
• Trace — 1× in this paper · explained in 2 episodes
• U-net architecture — 1× in this paper · explained in 3 episodes

Sources

• Imbalance in Regression Datasets
• Substructure Detection in Realistic Strong Lensing Systems with Machine Learning
• Dark Matter Subhalos, Strong Lensing and Machine Learning
• Detection of a dark matter subhalo in the strongly lensed system PJ011646

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