Machine learning using rapidity-mass matrices for event classification problems in HEP

Abstract

Supervised artificial neural networks with the rapidity-mass matrix (RMM) inputs were studied using several Monte Carlo event samples for various pp collision processes. The study shows the usability of this approach for general event classification problems. The proposed standardization of the ANN feature space can simplify searches for signatures of new physics at the LHC when using machine learning techniques. In particular, we illustrate how to improve signal-over-background ratios in searches for new physics, how to filter out Standard Model events for model-agnostic searches, and how to separate gluon and quark jets for Standard Model measurements.

21 pages, 8 figures

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Cited by in corpus (4)

• Event-based anomaly detection for new physics searches at the LHC using machine learning
• Imaging particle collision data for event classification using machine learning
• Enhancing the hunt for new phenomena in dijet final-states using anomaly detection filters at the High-Luminosity Large Hadron Collider
• Semi-supervised permutation invariant particle-level anomaly detection