most citedAnomaly preserving contrastive neural embeddings for end-to-end model-independent searches at the LHC

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hep-ph2026

Covariant Contrastive Learning for Uncertainty-Aware Anomaly Detection

Shelley Tong, Philip Harris, Gaia Grosso

Machine-learning-based anomaly detection (AD) offers a promising, model-agnostic alternative to traditional LHC analyses, allowing to search for many signals at once. Recent advanc…