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Found 6 papers, 3 papers with code
Interpreting Transformers for Jet Tagging
Machine learning (ML) algorithms, particularly attention-based transformer models, have become indispensable for analyzing the vast data generated by particle physics experiments like ATLAS and CMS at the CERN LHC.
Real-time Anomaly Detection at the L1 Trigger of CMS Experiment
We present the preparation, deployment, and testing of an autoencoder trained for unbiased detection of new physics signatures in the CMS experiment Global Trigger (GT) test crate FPGAs during LHC Run 3.
Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learning
Anomaly, or out-of-distribution, detection is a promising tool for aiding discoveries of new particles or processes in particle physics.
Fast Particle-based Anomaly Detection Algorithm with Variational Autoencoder
Model-agnostic anomaly detection is one of the promising approaches in the search for new beyond the standard model physics.
Efficient and Robust Jet Tagging at the LHC with Knowledge Distillation
The challenging environment of real-time data processing systems at the Large Hadron Collider (LHC) strictly limits the computational complexity of algorithms that can be deployed.
Physics Community Needs, Tools, and Resources for Machine Learning
Machine learning (ML) is becoming an increasingly important component of cutting-edge physics research, but its computational requirements present significant challenges.
