Application of a Convolutional Neural Network for image classification to the analysis of collisions in High Energy Physics

TL;DR

This paper explores using convolutional neural networks to classify particle collision events in High Energy Physics by transforming physical data into images, achieving competitive results with traditional methods.

Contribution

It introduces an intuitive image-based approach for collision classification and demonstrates its effectiveness on simulated CMS data at 7 TeV.

Findings

CNN approach yields competitive classification accuracy

Transforming physical variables into images is effective

Method performs well on simulated LHC collision data

Abstract

The application of deep learning techniques using convolutional neural networks to the classification of particle collisions in High Energy Physics is explored. An intuitive approach to transform physical variables, like momenta of particles and jets, into a single image that captures the relevant information, is proposed. The idea is tested using a well known deep learning framework on a simulation dataset, including leptonic ttbar events and the corresponding background at 7 TeV from the CMS experiment at LHC, available as Open Data. This initial test shows competitive results when compared to more classical approaches, like those using feedforward neural networks.