Abstract
The application of deep learning techniques using convolutional neural networks for 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.
Highlights
Deep learning with convolutional neural networks (CNNs) has revolutionized the world of computer vision and speech recognition over the last few years, yielding unprecedented performance in many machine learning tasks and opening a wide range of possibilities [1].In this paper, we explore a particular application of CNNs, image classification, in the context of analysis in experimental High Energy Physics (HEP)
We explore a particular application of CNNs, image classification, in the context of analysis in experimental High Energy Physics (HEP)
Boosted Decision Trees [3] and Feedforward Neural Networks [4] are much used in this context, but the latest state-of-the-art methods have not yet been fully explored and can bring a new light on the torrent of data being generated by experiments like those at the Large Hadron Collider (LHC) at CERN
Summary
Deep learning with convolutional neural networks (CNNs) has revolutionized the world of computer vision and speech recognition over the last few years, yielding unprecedented performance in many machine learning tasks and opening a wide range of possibilities [1].In this paper, we explore a particular application of CNNs, image classification, in the context of analysis in experimental High Energy Physics (HEP). Recent work has already successfully applied many ideas of the deep learning community to the HEP field [2]. Many studies in this field, including the search for new particles, require solving difficult signalversus-background classification problems, machine learning approaches are often adopted. We have tested the use of convolutional networks for the classification of collisions at LHC using Open Data Monte Carlo samples. The Compact Muon Solenoid (CMS) experiment [5] has pioneered, in the context of the LHC, in publicizing the collision data collected by the detector to the international community in order to carry out new analyses or to use them for training activities. CMS Open Data is available from the CERN Open Data portal and we have a dedicated portal developed in our center
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