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

TL;DR

This paper demonstrates that using rapidity-mass matrices as inputs for neural networks effectively classifies high-energy physics events, aiding in new physics searches and Standard Model measurements at the LHC.

Contribution

It introduces a standardized feature space with RMM inputs for neural networks, enhancing event classification and analysis in high-energy physics.

Findings

Effective classification of pp collision processes

Improved signal-to-background ratios in new physics searches

Enhanced separation of gluon and quark jets

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.