Imaging particle collision data for event classification using machine learning

Abstract

We propose a method to organize experimental data from particle collision experiments in a general format which can enable a simple visualisation and effective classification of collision data using machine learning techniques. The method is based on sparse fixed-size matrices with single- and two-particle variables containing information on identified particles and jets. We illustrate this method using an example of searches for new physics at the LHC experiments.

20 pages, 4 figures

References in corpus (8)

• PYTHIA 6.4 Physics and Manual
• LHAPDF6: parton density access in the LHC precision era
• Deep Learning and its Application to LHC Physics
• Dark Matter Searches at Colliders
• On measuring the masses of pair-produced semi-invisibly decaying particles at hadron colliders
• Ratios of dijet production cross sections as a function of the absolute difference in rapidity between jets in proton-proton collisions at sqrt(s) = 7 TeV
• HepSim: a repository with predictions for high-energy physics experiments
• Machine learning using rapidity-mass matrices for event classification problems in HEP

Cited by in corpus (8)

• Search for new phenomena in two-body invariant mass distributions using unsupervised machine learning for anomaly detection at $\sqrt{s}=13$ TeV with the ATLAS detector
• Event-based anomaly detection for new physics searches at the LHC using machine learning
• Enhancing the hunt for new phenomena in dijet final-states using anomaly detection filters at the High-Luminosity Large Hadron Collider
• Machine learning using rapidity-mass matrices for event classification problems in HEP
• Unsupervised and lightly supervised learning in particle physics
• ADFilter -- A Web Tool for New Physics Searches With Autoencoder-Based Anomaly Detection Using Deep Unsupervised Neural Networks
• Searches for new physics in collision events using a statistical technique for anomaly detection
• Jas4pp -- a Data-Analysis Framework for Physics and Detector Studies