Reconstruction of $\tau$ lepton pair invariant mass using an artificial neural network

The reconstruction of the invariant mass of τ lepton pairs is important for analyses containing Higgs and Z bosons decaying to τ+τ− , but highly challenging due to the neutrinos from the τ lepton decays, which cannot be measured in the detector. In this paper, we demonstrate how artificial neural ne...

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Detalles Bibliográficos
Autores principales: Bärtschi, P., Galloni, C., Lange, C., Kilminster, B.
Lenguaje:eng
Publicado: 2019
Materias:
hep-ex
Particle Physics - Experiment
Acceso en línea:https://dx.doi.org/10.1016/j.nima.2019.03.029
http://cds.cern.ch/record/2671503
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author Bärtschi, P.
Galloni, C.
Lange, C.
Kilminster, B.
author_facet Bärtschi, P.
Galloni, C.
Lange, C.
Kilminster, B.
author_sort Bärtschi, P.
collection CERN
description The reconstruction of the invariant mass of τ lepton pairs is important for analyses containing Higgs and Z bosons decaying to τ+τ− , but highly challenging due to the neutrinos from the τ lepton decays, which cannot be measured in the detector. In this paper, we demonstrate how artificial neural networks can be used to reconstruct the mass of a di- τ system and compare this procedure to an algorithm used by the CMS Collaboration for this purpose. We find that the neural network output shows a smaller bias and better resolution of the di- τ mass reconstruction and an improved discrimination between a Higgs boson signal and the Drell–Yan background with a much shorter computation time.
id cern-2671503
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2019
record_format invenio
spelling cern-26715032023-06-29T04:25:24Zdoi:10.1016/j.nima.2019.03.029doi:10.1016/j.nima.2019.03.029http://cds.cern.ch/record/2671503engBärtschi, P.Galloni, C.Lange, C.Kilminster, B.Reconstruction of $\tau$ lepton pair invariant mass using an artificial neural networkhep-exParticle Physics - ExperimentThe reconstruction of the invariant mass of τ lepton pairs is important for analyses containing Higgs and Z bosons decaying to τ+τ− , but highly challenging due to the neutrinos from the τ lepton decays, which cannot be measured in the detector. In this paper, we demonstrate how artificial neural networks can be used to reconstruct the mass of a di- τ system and compare this procedure to an algorithm used by the CMS Collaboration for this purpose. We find that the neural network output shows a smaller bias and better resolution of the di- τ mass reconstruction and an improved discrimination between a Higgs boson signal and the Drell–Yan background with a much shorter computation time.The reconstruction of the invariant mass of $\tau$ lepton pairs is important for analyses containing Higgs and Z bosons decaying to $\tau^{+}\tau^{-}$, but highly challenging due to the neutrinos from the $\tau$ lepton decays, which cannot be measured in the detector. In this paper, we demonstrate how artificial neural networks can be used to reconstruct the mass of a di-$\tau$ system and compare this procedure to an algorithm used by the CMS Collaboration for this purpose. We find that the neural network output shows a smaller bias and better resolution of the di-$\tau$ mass reconstruction and an improved discrimination between a Higgs boson signal and the Drell-Yan background with a much shorter computation time.arXiv:1904.04924oai:cds.cern.ch:26715032019-04-09
spellingShingle hep-ex
Particle Physics - Experiment
Bärtschi, P.
Galloni, C.
Lange, C.
Kilminster, B.
Reconstruction of $\tau$ lepton pair invariant mass using an artificial neural network
title Reconstruction of $\tau$ lepton pair invariant mass using an artificial neural network
title_full Reconstruction of $\tau$ lepton pair invariant mass using an artificial neural network
title_fullStr Reconstruction of $\tau$ lepton pair invariant mass using an artificial neural network
title_full_unstemmed Reconstruction of $\tau$ lepton pair invariant mass using an artificial neural network
title_short Reconstruction of $\tau$ lepton pair invariant mass using an artificial neural network
title_sort reconstruction of $\tau$ lepton pair invariant mass using an artificial neural network
topic hep-ex
Particle Physics - Experiment
url https://dx.doi.org/10.1016/j.nima.2019.03.029
https://dx.doi.org/10.1016/j.nima.2019.03.029
http://cds.cern.ch/record/2671503
work_keys_str_mv AT bartschip reconstructionoftauleptonpairinvariantmassusinganartificialneuralnetwork
AT gallonic reconstructionoftauleptonpairinvariantmassusinganartificialneuralnetwork
AT langec reconstructionoftauleptonpairinvariantmassusinganartificialneuralnetwork
AT kilminsterb reconstructionoftauleptonpairinvariantmassusinganartificialneuralnetwork

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