Imaging particle collision data for event classification using machine learning

TL;DR

This paper introduces a method to organize particle collision data into fixed-size matrices, facilitating visualization and classification with machine learning, demonstrated through LHC physics searches.

Contribution

It presents a novel data organization technique using sparse matrices for effective machine learning-based classification of collision events.

Findings

Enables visualization of collision data

Improves classification accuracy with machine learning

Applicable to new physics searches at LHC

Abstract

We propose a method to organize experimental data from particle collision experiments in a general format which can enable a simple visualisation and effective classification of collision data using machine learning techniques. The method is based on sparse fixed-size matrices with single- and two-particle variables containing information on identified particles and jets. We illustrate this method using an example of searches for new physics at the LHC experiments.