4 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…
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…
Insights into Dark Matter Direct Detection Experiments: Decision Trees versus Deep Learning
Daniel E. Lopez-Fogliani, Andres D. Perez, Roberto Ruiz de Austri
The detection of Dark Matter (DM) remains a significant challenge in particle physics. This study exploits advanced machine learning models to improve detection capabilities of liq…
Large Physics Models: Towards a collaborative approach with Large Language Models and Foundation Models
Kristian G. Barman, Sascha Caron, Emily Sullivan +19
This paper explores ideas and provides a potential roadmap for the development and evaluation of physics-specific large-scale AI models, which we call Large Physics Models (LPMs).…