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Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/87443
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dc.contributor.authorBuckley, A.-
dc.contributor.authorShilton, A.-
dc.contributor.authorWhite, M.-
dc.date.issued2012-
dc.identifier.citationComputer Physics Communications, 2012; 183(4):960-970-
dc.identifier.issn0010-4655-
dc.identifier.issn1879-2944-
dc.identifier.urihttp://hdl.handle.net/2440/87443-
dc.description.abstractAbstract not available-
dc.description.statementofresponsibilityA. Buckley, A. Shilton, M.J. White-
dc.language.isoen-
dc.publisherElsevier Science-
dc.rights© 2012 Elsevier B.V. All rights reserved.-
dc.source.urihttp://dx.doi.org/10.1016/j.cpc.2011.12.026-
dc.subjectSupersymmetry phenomenology; Large Hadron Collider-
dc.titleFast supersymmetry phenomenology at the Large Hadron Collider using machine learning techniques-
dc.typeJournal article-
dc.identifier.doi10.1016/j.cpc.2011.12.026-
dc.relation.granthttp://purl.org/au-research/grants/arc/DP1095099-
pubs.publication-statusPublished-
dc.identifier.orcidWhite, M. [0000-0001-5474-4580]-
Appears in Collections:Aurora harvest 7
Chemistry and Physics publications

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