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

Autor:	Madrazo, Celia Fernández, Cacha, Ignacio Heredia, Iglesias, Lara Lloret, de Lucas, Jesús Marco
Rok vydání:	2017
Předmět:	Computer Science - Computer Vision and Pattern Recognition High Energy Physics - Experiment
Druh dokumentu:	Working Paper
Popis:	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. Comment: 14 pages, 8 figures, educational
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1708.07034 Zobrazit plný text záznamu View this record from Arxiv