Covariant Contrastive Learning for Uncertainty-Aware Anomaly Detection
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 advances in representation learning motivate the use of neural embeddings to map high-dimensional physics observables into low-dimensional latent...