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dc.contributor.authorKvellestad, Anders
dc.contributor.authorMæland, Steffen
dc.contributor.authorStrümke, Inga
dc.PublishedKvellestad A, Mæland S, Strümke I. Signal mixture estimation for degenerate heavy Higgses using a deep neural network. European Physical Journal C. 2018;78:1010eng
dc.description.abstractIf a new signal is established in future LHC data, a next question will be to determine the signal composition, in particular whether the signal is due to multiple near-degenerate states. We investigate the performance of a deep learning approach to signal mixture estimation for the challenging scenario of a ditau signal coming from a pair of degenerate Higgs bosons of opposite CP charge. This constitutes a parameter estimation problem for a mixture model with highly overlapping features. We use an unbinned maximum likelihood fit to a neural network output, and compare the results to mixture estimation via a fit to a single kinematic variable. For our benchmark scenarios we find a ∼20% improvement in the estimate uncertainty.en_US
dc.relation.ispartof<a href="" target="blank"> Parameter Scans and Machine Learning for beyond Standard Model Physics</a>en_US
dc.rightsAttribution CC BYeng
dc.titleSignal mixture estimation for degenerate heavy Higgses using a deep neural networken_US
dc.typePeer reviewed
dc.typeJournal article
dc.rights.holderCopyright 2018 The Author(s)en_US
dc.source.journalEuropean Physical Journal C
dc.relation.projectNorges forskningsråd: 230546

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