DeeLeMa: Missing information search with Deep Learning for Mass estimation

We present DeeLeMa, a deep learning network to analyze energies and momenta in particle collisions at high energy colliders, especially DeeLeMa is constructed based on symmetric event topology, and the generated mass distributions show robust peaks at the physical masses after the combinatoric uncer...

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Detalles Bibliográficos
Autores principales: Ban, Kayoung, Kang, Dong Woo, Kim, Tae Geun, Park, Seong Chan, Park, Yeji
Lenguaje:eng
Publicado: 2022
Materias:
hep-ph
Particle Physics - Phenomenology
Acceso en línea:http://cds.cern.ch/record/2846004
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author Ban, Kayoung
Kang, Dong Woo
Kim, Tae Geun
Park, Seong Chan
Park, Yeji
author_facet Ban, Kayoung
Kang, Dong Woo
Kim, Tae Geun
Park, Seong Chan
Park, Yeji
author_sort Ban, Kayoung
collection CERN
description We present DeeLeMa, a deep learning network to analyze energies and momenta in particle collisions at high energy colliders, especially DeeLeMa is constructed based on symmetric event topology, and the generated mass distributions show robust peaks at the physical masses after the combinatoric uncertainties, and detector smearing effects are taken into account. DeeLeMa can be widely used in different event topologies by adopting the corresponding kinematic symmetries.
id cern-2846004
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2022
record_format invenio
spelling cern-28460042023-01-31T11:13:47Zhttp://cds.cern.ch/record/2846004engBan, KayoungKang, Dong WooKim, Tae GeunPark, Seong ChanPark, YejiDeeLeMa: Missing information search with Deep Learning for Mass estimationhep-phParticle Physics - PhenomenologyWe present DeeLeMa, a deep learning network to analyze energies and momenta in particle collisions at high energy colliders, especially DeeLeMa is constructed based on symmetric event topology, and the generated mass distributions show robust peaks at the physical masses after the combinatoric uncertainties, and detector smearing effects are taken into account. DeeLeMa can be widely used in different event topologies by adopting the corresponding kinematic symmetries.arXiv:2212.12836CERN-TH-2022-218KIAS-P22085oai:cds.cern.ch:28460042022-12-24
spellingShingle hep-ph
Particle Physics - Phenomenology
Ban, Kayoung
Kang, Dong Woo
Kim, Tae Geun
Park, Seong Chan
Park, Yeji
DeeLeMa: Missing information search with Deep Learning for Mass estimation
title DeeLeMa: Missing information search with Deep Learning for Mass estimation
title_full DeeLeMa: Missing information search with Deep Learning for Mass estimation
title_fullStr DeeLeMa: Missing information search with Deep Learning for Mass estimation
title_full_unstemmed DeeLeMa: Missing information search with Deep Learning for Mass estimation
title_short DeeLeMa: Missing information search with Deep Learning for Mass estimation
title_sort deelema: missing information search with deep learning for mass estimation
topic hep-ph
Particle Physics - Phenomenology
url http://cds.cern.ch/record/2846004
work_keys_str_mv AT bankayoung deelemamissinginformationsearchwithdeeplearningformassestimation
AT kangdongwoo deelemamissinginformationsearchwithdeeplearningformassestimation
AT kimtaegeun deelemamissinginformationsearchwithdeeplearningformassestimation
AT parkseongchan deelemamissinginformationsearchwithdeeplearningformassestimation
AT parkyeji deelemamissinginformationsearchwithdeeplearningformassestimation

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