Signal mixture estimation for degenerate heavy Higgses using a deep neural network

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 scen...

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Veröffentlicht in:	The European physical journal. C, Particles and fields Jg. 78; H. 12; S. 1 - 11
Hauptverfasser:	Kvellestad, Anders, Maeland, Steffen, Strümke, Inga
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2018 Springer Springer Nature B.V SpringerOpen
Schlagworte:	Artificial neural networks Astronomy Astrophysics and Cosmology Bosons Elementary Particles Hadrons Heavy Ions Higgs bosons Machine learning Measurement Science and Instrumentation Neural networks Nuclear Energy Nuclear Physics Parameter estimation Physics Physics and Astronomy Quantum Field Theories Quantum Field Theory Regular Article - Experimental Physics String Theory
ISSN:	1434-6044, 1434-6052
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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.
AbstractList	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 \[\sim 20\%\] improvement in the estimate uncertainty. 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 [Formula omitted] improvement in the estimate uncertainty. 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. 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 $$\sim 20\%$$ ∼20% improvement in the estimate uncertainty.
ArticleNumber	1010
Audience	Academic
Author	Kvellestad, Anders Strümke, Inga Maeland, Steffen
Author_xml	– sequence: 1 givenname: Anders orcidid: 0000-0002-5267-7705 surname: Kvellestad fullname: Kvellestad, Anders organization: Department of Physics, University of Oslo, Blackett Laboratory, Department of Physics, Imperial College London – sequence: 2 givenname: Steffen orcidid: 0000-0002-4652-4753 surname: Maeland fullname: Maeland, Steffen email: steffen.maeland@uib.no organization: Department of Physics and Technology, University of Bergen – sequence: 3 givenname: Inga orcidid: 0000-0003-1820-6544 surname: Strümke fullname: Strümke, Inga email: inga.strumke@uib.no organization: Department of Physics and Technology, University of Bergen
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Snippet	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... 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...
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SubjectTerms	Artificial neural networks Astronomy Astrophysics and Cosmology Bosons Elementary Particles Hadrons Heavy Ions Higgs bosons Machine learning Measurement Science and Instrumentation Neural networks Nuclear Energy Nuclear Physics Parameter estimation Physics Physics and Astronomy Quantum Field Theories Quantum Field Theory Regular Article - Experimental Physics String Theory
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Title	Signal mixture estimation for degenerate heavy Higgses using a deep neural network
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