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

Source: [Application of a Convolutional Neural Network for image classification to the analysis of collisions in High Energy Physics](<https://www.sota2.com/research/paper/68fa363e1e209131ce8a8281>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-24T21:43:01.688Z

Authors: Celia Fern\\'andez Madrazo, Ignacio Heredia Cacha, Lara Lloret Iglesias, Jes\\'us Marco de Lucas

Published: 2017-08-23T15:03:08.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1708.07034>)

[PDF](<https://arxiv.org/pdf/1708.07034.pdf>)