NNDrone: a toolkit for the mass application of machine learning in High Energy Physics

Machine learning has proven to be an indispensable tool in the selection of interesting events in high energy physics. Such technologies will become increasingly important as detector upgrades are introduced and data rates increase by orders of magnitude. We propose a toolkit to enable the creation...

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Bibliographic Details
Published inarXiv.org
Main Authors Benson, Sean, Gizdov, Konstantin
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 12.02.2019
Subjects
Artificial intelligence
Classifiers
High energy physics
Machine learning
Neural networks
Physics - Data Analysis, Statistics and Probability
Physics - High Energy Physics - Experiment
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Summary:Machine learning has proven to be an indispensable tool in the selection of interesting events in high energy physics. Such technologies will become increasingly important as detector upgrades are introduced and data rates increase by orders of magnitude. We propose a toolkit to enable the creation of a drone classifier from any machine learning classifier, such that different classifiers may be standardised into a single form and executed in parallel. We demonstrate the capability of the drone neural network to learn the required properties of the input neural network without the use of any labels from the training data, only using appropriate questioning of the input neural network.
ISSN:2331-8422
DOI:10.48550/arxiv.1712.09114