dc.contributor.author | Kvellestad, Anders | |
dc.contributor.author | Mæland, Steffen | |
dc.contributor.author | Strümke, Inga | |
dc.date.accessioned | 2019-06-25T12:14:20Z | |
dc.date.available | 2019-06-25T12:14:20Z | |
dc.date.issued | 2018-12 | |
dc.Published | Kvellestad A, Mæland S, Strümke I. Signal mixture estimation for degenerate heavy Higgses using a deep neural network. European Physical Journal C. 2018;78:1010 | eng |
dc.identifier.issn | 1434-6044 | en_US |
dc.identifier.issn | 1434-6052 | en_US |
dc.identifier.uri | https://hdl.handle.net/1956/20405 | |
dc.description.abstract | If 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.language.iso | eng | eng |
dc.publisher | Springer | en_US |
dc.relation.ispartof | <a href="http://hdl.handle.net/1956/20546" target="blank"> Parameter Scans and Machine Learning for beyond Standard Model Physics</a> | en_US |
dc.rights | Attribution CC BY | eng |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0 | eng |
dc.title | Signal mixture estimation for degenerate heavy Higgses using a deep neural network | en_US |
dc.type | Peer reviewed | |
dc.type | Journal article | |
dc.date.updated | 2019-01-31T13:21:41Z | |
dc.description.version | publishedVersion | en_US |
dc.rights.holder | Copyright 2018 The Author(s) | en_US |
dc.identifier.doi | https://doi.org/10.1140/epjc/s10052-018-6455-z | |
dc.identifier.cristin | 1643838 | |
dc.source.journal | European Physical Journal C | |
dc.relation.project | Norges forskningsråd: 230546 | |