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

1 citations · 1 across the 2 of their papers we have counts for

collaborators

3 papers

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…

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)…