Machine learning techniques for detecting topological avatars of new physics

The search for highly ionizing particles in nuclear track detectors (NTDs) traditionally requires experts to manually search through samples in order to identify regions of interest that could be a hint of physics beyond the standard model of particle physics. The advent of automated image acquisiti...

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Published inPhilosophical transactions of the Royal Society of London. Series A: Mathematical, physical, and engineering sciences Vol. 377; no. 2161; p. 20190392
Main Author Bevan, A. J.
Format Journal Article
LanguageEnglish
Published 30.12.2019
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Summary:The search for highly ionizing particles in nuclear track detectors (NTDs) traditionally requires experts to manually search through samples in order to identify regions of interest that could be a hint of physics beyond the standard model of particle physics. The advent of automated image acquisition and modern data science, including machine learning-based processing of data presents an opportunity to accelerate the process of searching for anomalies in NTDs that could be a hint of a new physics avatar. The potential for modern data science applied to this topic in the context of the MoEDAL experiment at the large Hadron collider at the European Centre for Nuclear Research, CERN, is discussed. This article is part of a discussion meeting issue ‘Topological avatars of new physics’.
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ISSN:1364-503X
1471-2962
DOI:10.1098/rsta.2019.0392