5 papers
Hits to Higgs: Hit-Level Higgs Classification from Raw LHC Detector Data Using Higgsformer
Sascha Caron, Polina Moskvitina, Roberto Ruiz de Austri +1
We present Higgsformer, a transformer-based architecture that classifies Higgs events at the Large Hadron Collider directly from raw inner tracker hits, bypassing the traditional r…
Masked-Token Prediction for Anomaly Detection at the Large Hadron Collider
Ambre Visive, Roberto Ruiz de Austri, Polina Moskvitina +2
Anomaly detection in High Energy Physics requires identifying rare signals against overwhelming backgrounds, without prior knowledge of the signal. We present the first application…
Event Tokenization and Masked-Token Prediction for Anomaly Detection at the Large Hadron Collider
Ambre Visive, Polina Moskvitina, Clara Nellist +2
We propose a novel use of Large Language Models (LLMs) as unsupervised anomaly detectors in particle physics. Using lightweight LLM-like networks with encoder-based architectures t…
Attention to the strengths of physical interactions: Transformer and graph-based event classification for particle physics experiments
Luc Builtjes, Sascha Caron, Polina Moskvitina +4
A major task in particle physics is the measurement of rare signal processes. Even modest improvements in background rejection, at a fixed signal efficiency, can significantly enha…
Universal Anomaly Detection at the LHC: Transforming Optimal Classifiers and the DDD Method
Sascha Caron, José Enrique GarcÃa Navarro, MarÃa Moreno Llácer +5
In this work, we present a novel approach to transform supervised classifiers into effective unsupervised anomaly detectors. The method we have developed, termed Discriminatory Det…