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

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hep-ex2025★ 1 cited

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

Kyle Metzger, Lana Xu, Mia Sodini +4

Anomaly detection - identifying deviations from Standard Model predictions - is a key challenge at the Large Hadron Collider due to the size and complexity of its datasets. This is…

hep-ex2025

Robust resonant anomaly detection with NPLM

Gaia Grosso, Debajyoti Sengupta, Tobias Golling +1

In this study, we investigate the application of the New Physics Learning Machine (NPLM) algorithm as an alternative to the standard CWoLa method with Boosted Decision Trees (BDTs)…