Training Deep 3D Convolutional Neural Networks to Extract BSM Physics Parameters Directly from HEP D...
Training Deep 3D Convolutional Neural Networks to Extract BSM Physics Parameters Directly from HEP Data: a Proof-of-Concept Study Using Monte Carlo Simulations
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Author / Creator
Dubey, S , Browder, T E , Kohani, S , Mandal, R , Sibidanov, A and Sinha, R
Publisher
Ithaca: Cornell University Library, arXiv.org
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Language
English
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Publisher
Ithaca: Cornell University Library, arXiv.org
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Contents
We report on a novel application of computer vision techniques to extract beyond the Standard Model parameters directly from high energy physics flavor data. We propose a simple but novel data representation that transforms the angular and kinematic distributions into "quasi-images", which are used to train a convolutional neural network to perform...
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Full title
Training Deep 3D Convolutional Neural Networks to Extract BSM Physics Parameters Directly from HEP Data: a Proof-of-Concept Study Using Monte Carlo Simulations
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TN_cdi_proquest_journals_2892788640
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2892788640
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E-ISSN
2331-8422
