dc.contributor.author | Bevan, AJ | en_US |
dc.date.accessioned | 2020-01-14T14:13:39Z | |
dc.date.available | 2019-09-23 | en_US |
dc.date.issued | 2019-12-30 | en_US |
dc.identifier.issn | 1364-503X | en_US |
dc.identifier.other | ARTN 20190392 | en_US |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/62385 | |
dc.relation.ispartof | PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES | en_US |
dc.rights | Published by the Royal Society. All rights reserved. | |
dc.subject | monopoles | en_US |
dc.subject | deep learning | en_US |
dc.subject | convolutional neural networks | en_US |
dc.subject | nuclear track detectors | en_US |
dc.title | Machine learning techniques for detecting topological avatars of new physics | en_US |
dc.type | Article | |
dc.rights.holder | © 2019 The Author(s) | |
dc.identifier.doi | 10.1098/rsta.2019.0392 | en_US |
pubs.author-url | http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000511612700013&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=612ae0d773dcbdba3046f6df545e9f6a | en_US |
pubs.issue | 2161 | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Published | en_US |
pubs.volume | 377 | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |
qmul.funder | Detector Development Infrastructure::Science and Technology Facilities Council [2006-2012] | en_US |
qmul.funder | Detector Development Infrastructure::Science and Technology Facilities Council [2006-2012] | en_US |