Bayesian probabilistic modeling for four-top production at the LHC

Autores
Alvarez, Ezequiel; Dillon, Barry M.; Faroughy, Darius A.; Kamenik, Jernej F.; Lamagna, Federico Agustín; Szewc, Manuel
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Monte Carlo (MC) generators are crucial for analyzing data in particle collider experiments. However, often even a small mismatch between the MC simulations and the measurements can undermine the interpretation of the results. This is particularly important in the context of LHC searches for rare physics processes within and beyond the standard model (SM). One of the ultimate rare processes in the SM currently being explored at the LHC, pp→tt¯tt¯ with its large multidimensional phase-space is an ideal testing ground to explore new ways to reduce the impact of potential MC mismodeling on experimental results. We propose a novel statistical method capable of disentangling the 4-top signal from the dominant backgrounds in the same-sign dilepton channel, while simultaneously correcting for possible MC imperfections in modeling of the most relevant discriminating observables - the jet multiplicity distributions. A Bayesian mixture of multinomials is used to model the light-jet and b-jet multiplicities under the assumption of their conditional independence. The signal and background distributions generated from a deliberately mistuned MC simulator are used as model priors. The posterior distributions, as well as the signal and background fractions, are then learned from the data using Bayesian inference. We demonstrate that our method can mitigate the effects of large MC mismodelings in the context of a realistic tt¯tt¯ search, leading to corrected posterior distributions that better approximate the underlying truth-level spectra.
Fil: Alvarez, Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias Físicas. - Universidad Nacional de San Martín. Instituto de Ciencias Físicas; Argentina
Fil: Dillon, Barry M.. Ruprecht Karls Universitat Heidelberg; Alemania
Fil: Faroughy, Darius A.. Universitat Zurich; Suiza
Fil: Kamenik, Jernej F.. Universitat Zurich; Suiza
Fil: Lamagna, Federico Agustín. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro. Archivo Histórico del Centro Atómico Bariloche e Instituto Balseiro | Universidad Nacional de Cuyo. Instituto Balseiro. Archivo Histórico del Centro Atómico Bariloche e Instituto Balseiro; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Szewc, Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias Físicas. - Universidad Nacional de San Martín. Instituto de Ciencias Físicas; Argentina
Materia
lhc
top
bayesian inference
Nj and Nb
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/212924
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Bayesian probabilistic modeling for four-top production at the LHCAlvarez, EzequielDillon, Barry M.Faroughy, Darius A.Kamenik, Jernej F.Lamagna, Federico AgustínSzewc, Manuellhctopbayesian inferenceNj and Nbhttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Monte Carlo (MC) generators are crucial for analyzing data in particle collider experiments. However, often even a small mismatch between the MC simulations and the measurements can undermine the interpretation of the results. This is particularly important in the context of LHC searches for rare physics processes within and beyond the standard model (SM). One of the ultimate rare processes in the SM currently being explored at the LHC, pp→tt¯tt¯ with its large multidimensional phase-space is an ideal testing ground to explore new ways to reduce the impact of potential MC mismodeling on experimental results. We propose a novel statistical method capable of disentangling the 4-top signal from the dominant backgrounds in the same-sign dilepton channel, while simultaneously correcting for possible MC imperfections in modeling of the most relevant discriminating observables - the jet multiplicity distributions. A Bayesian mixture of multinomials is used to model the light-jet and b-jet multiplicities under the assumption of their conditional independence. The signal and background distributions generated from a deliberately mistuned MC simulator are used as model priors. The posterior distributions, as well as the signal and background fractions, are then learned from the data using Bayesian inference. We demonstrate that our method can mitigate the effects of large MC mismodelings in the context of a realistic tt¯tt¯ search, leading to corrected posterior distributions that better approximate the underlying truth-level spectra.Fil: Alvarez, Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias Físicas. - Universidad Nacional de San Martín. Instituto de Ciencias Físicas; ArgentinaFil: Dillon, Barry M.. Ruprecht Karls Universitat Heidelberg; AlemaniaFil: Faroughy, Darius A.. Universitat Zurich; SuizaFil: Kamenik, Jernej F.. Universitat Zurich; SuizaFil: Lamagna, Federico Agustín. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro. Archivo Histórico del Centro Atómico Bariloche e Instituto Balseiro | Universidad Nacional de Cuyo. Instituto Balseiro. Archivo Histórico del Centro Atómico Bariloche e Instituto Balseiro; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Szewc, Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias Físicas. - Universidad Nacional de San Martín. Instituto de Ciencias Físicas; ArgentinaAmerican Physical Society2022-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/212924Alvarez, Ezequiel; Dillon, Barry M.; Faroughy, Darius A.; Kamenik, Jernej F.; Lamagna, Federico Agustín; et al.; Bayesian probabilistic modeling for four-top production at the LHC; American Physical Society; Physical Review D; 105; 9; 5-2022; 1-110556-28212470-0029CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1103/PhysRevD.105.092001info:eu-repo/semantics/altIdentifier/url/https://journals.aps.org/prd/abstract/10.1103/PhysRevD.105.092001info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:09:50Zoai:ri.conicet.gov.ar:11336/212924instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-10 13:09:50.972CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Bayesian probabilistic modeling for four-top production at the LHC
title Bayesian probabilistic modeling for four-top production at the LHC
spellingShingle Bayesian probabilistic modeling for four-top production at the LHC
Alvarez, Ezequiel
lhc
top
bayesian inference
Nj and Nb
title_short Bayesian probabilistic modeling for four-top production at the LHC
title_full Bayesian probabilistic modeling for four-top production at the LHC
title_fullStr Bayesian probabilistic modeling for four-top production at the LHC
title_full_unstemmed Bayesian probabilistic modeling for four-top production at the LHC
title_sort Bayesian probabilistic modeling for four-top production at the LHC
dc.creator.none.fl_str_mv Alvarez, Ezequiel
Dillon, Barry M.
Faroughy, Darius A.
Kamenik, Jernej F.
Lamagna, Federico Agustín
Szewc, Manuel
author Alvarez, Ezequiel
author_facet Alvarez, Ezequiel
Dillon, Barry M.
Faroughy, Darius A.
Kamenik, Jernej F.
Lamagna, Federico Agustín
Szewc, Manuel
author_role author
author2 Dillon, Barry M.
Faroughy, Darius A.
Kamenik, Jernej F.
Lamagna, Federico Agustín
Szewc, Manuel
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv lhc
top
bayesian inference
Nj and Nb
topic lhc
top
bayesian inference
Nj and Nb
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Monte Carlo (MC) generators are crucial for analyzing data in particle collider experiments. However, often even a small mismatch between the MC simulations and the measurements can undermine the interpretation of the results. This is particularly important in the context of LHC searches for rare physics processes within and beyond the standard model (SM). One of the ultimate rare processes in the SM currently being explored at the LHC, pp→tt¯tt¯ with its large multidimensional phase-space is an ideal testing ground to explore new ways to reduce the impact of potential MC mismodeling on experimental results. We propose a novel statistical method capable of disentangling the 4-top signal from the dominant backgrounds in the same-sign dilepton channel, while simultaneously correcting for possible MC imperfections in modeling of the most relevant discriminating observables - the jet multiplicity distributions. A Bayesian mixture of multinomials is used to model the light-jet and b-jet multiplicities under the assumption of their conditional independence. The signal and background distributions generated from a deliberately mistuned MC simulator are used as model priors. The posterior distributions, as well as the signal and background fractions, are then learned from the data using Bayesian inference. We demonstrate that our method can mitigate the effects of large MC mismodelings in the context of a realistic tt¯tt¯ search, leading to corrected posterior distributions that better approximate the underlying truth-level spectra.
Fil: Alvarez, Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias Físicas. - Universidad Nacional de San Martín. Instituto de Ciencias Físicas; Argentina
Fil: Dillon, Barry M.. Ruprecht Karls Universitat Heidelberg; Alemania
Fil: Faroughy, Darius A.. Universitat Zurich; Suiza
Fil: Kamenik, Jernej F.. Universitat Zurich; Suiza
Fil: Lamagna, Federico Agustín. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro. Archivo Histórico del Centro Atómico Bariloche e Instituto Balseiro | Universidad Nacional de Cuyo. Instituto Balseiro. Archivo Histórico del Centro Atómico Bariloche e Instituto Balseiro; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Szewc, Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias Físicas. - Universidad Nacional de San Martín. Instituto de Ciencias Físicas; Argentina
description Monte Carlo (MC) generators are crucial for analyzing data in particle collider experiments. However, often even a small mismatch between the MC simulations and the measurements can undermine the interpretation of the results. This is particularly important in the context of LHC searches for rare physics processes within and beyond the standard model (SM). One of the ultimate rare processes in the SM currently being explored at the LHC, pp→tt¯tt¯ with its large multidimensional phase-space is an ideal testing ground to explore new ways to reduce the impact of potential MC mismodeling on experimental results. We propose a novel statistical method capable of disentangling the 4-top signal from the dominant backgrounds in the same-sign dilepton channel, while simultaneously correcting for possible MC imperfections in modeling of the most relevant discriminating observables - the jet multiplicity distributions. A Bayesian mixture of multinomials is used to model the light-jet and b-jet multiplicities under the assumption of their conditional independence. The signal and background distributions generated from a deliberately mistuned MC simulator are used as model priors. The posterior distributions, as well as the signal and background fractions, are then learned from the data using Bayesian inference. We demonstrate that our method can mitigate the effects of large MC mismodelings in the context of a realistic tt¯tt¯ search, leading to corrected posterior distributions that better approximate the underlying truth-level spectra.
publishDate 2022
dc.date.none.fl_str_mv 2022-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/212924
Alvarez, Ezequiel; Dillon, Barry M.; Faroughy, Darius A.; Kamenik, Jernej F.; Lamagna, Federico Agustín; et al.; Bayesian probabilistic modeling for four-top production at the LHC; American Physical Society; Physical Review D; 105; 9; 5-2022; 1-11
0556-2821
2470-0029
CONICET Digital
CONICET
url http://hdl.handle.net/11336/212924
identifier_str_mv Alvarez, Ezequiel; Dillon, Barry M.; Faroughy, Darius A.; Kamenik, Jernej F.; Lamagna, Federico Agustín; et al.; Bayesian probabilistic modeling for four-top production at the LHC; American Physical Society; Physical Review D; 105; 9; 5-2022; 1-11
0556-2821
2470-0029
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1103/PhysRevD.105.092001
info:eu-repo/semantics/altIdentifier/url/https://journals.aps.org/prd/abstract/10.1103/PhysRevD.105.092001
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv American Physical Society
publisher.none.fl_str_mv American Physical Society
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.993085

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