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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Bibliographic Details
Published in:The European physical journal. C, Particles and fields Vol. 78; no. 12; pp. 1 - 11
Main Authors: Kvellestad, Anders, Maeland, Steffen, Strümke, Inga
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2018
Springer
Springer Nature B.V
SpringerOpen
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:1434-6044
1434-6052
DOI:10.1140/epjc/s10052-018-6455-z