Application of a Convolutional Neural Network for image classification to the analysis of collisions in High Energy Physics
This work addresses collision analysis for High Energy Physics researchers, but it is incremental as it adapts existing deep learning methods to a specific domain.
The paper tackled the classification of particle collisions in High Energy Physics by transforming physical variables into images and applying a convolutional neural network, achieving competitive results compared to classical feedforward neural networks on a simulation dataset.
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.