Exploring unsupervised top tagging using Bayesian inference
- Autores
- Alvarez, Ezequiel; Szewc, Manuel; Szynkman, Alejandro Andrés; Tanco, Santiago Andrés; Tarutina, Tatiana
- Año de publicación
- 2023
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Recognizing hadronically decaying top-quark jets in a sample of jets, or even its total fraction in the sample, is an important step in many LHC searches for Standard Model and Beyond Standard Model physics as well. Although there exists outstanding top-tagger algorithms, their construction and their expected performance rely on Montecarlo simulations, which may induce potential biases. For these reasons we develop two simple unsupervised top-tagger algorithms based on performing Bayesian inference on a mixture model. In one of them we use as the observed variable a new geometrically-based observable Ã3, and in the other we consider the more traditional τ3/τ2 N-subjettiness ratio, which yields a better performance. As expected, we find that the unsupervised tagger performance is below existing supervised taggers, reaching expected Area Under Curve AUC ∼ 0.80 − 0.81 and accuracies of about 69% − 75% in a full range of sample purity. However, these performances are more robust to possible biases in the Montecarlo that their supervised counterparts. Our findings are a step towards exploring and considering simpler and unbiased taggers.
Fil: Alvarez, Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Szewc, Manuel. University of Cincinnati; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Szynkman, Alejandro Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; Argentina
Fil: Tanco, Santiago Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; Argentina
Fil: Tarutina, Tatiana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; Argentina - Materia
-
Jets
machine learning
top quark - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/243762
Ver los metadatos del registro completo
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Exploring unsupervised top tagging using Bayesian inferenceAlvarez, EzequielSzewc, ManuelSzynkman, Alejandro AndrésTanco, Santiago AndrésTarutina, TatianaJetsmachine learningtop quarkhttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Recognizing hadronically decaying top-quark jets in a sample of jets, or even its total fraction in the sample, is an important step in many LHC searches for Standard Model and Beyond Standard Model physics as well. Although there exists outstanding top-tagger algorithms, their construction and their expected performance rely on Montecarlo simulations, which may induce potential biases. For these reasons we develop two simple unsupervised top-tagger algorithms based on performing Bayesian inference on a mixture model. In one of them we use as the observed variable a new geometrically-based observable Ã3, and in the other we consider the more traditional τ3/τ2 N-subjettiness ratio, which yields a better performance. As expected, we find that the unsupervised tagger performance is below existing supervised taggers, reaching expected Area Under Curve AUC ∼ 0.80 − 0.81 and accuracies of about 69% − 75% in a full range of sample purity. However, these performances are more robust to possible biases in the Montecarlo that their supervised counterparts. Our findings are a step towards exploring and considering simpler and unbiased taggers.Fil: Alvarez, Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Szewc, Manuel. University of Cincinnati; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Szynkman, Alejandro Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; ArgentinaFil: Tanco, Santiago Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; ArgentinaFil: Tarutina, Tatiana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; ArgentinaSciPost Foundation2023-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/243762Alvarez, Ezequiel; Szewc, Manuel; Szynkman, Alejandro Andrés; Tanco, Santiago Andrés; Tarutina, Tatiana; Exploring unsupervised top tagging using Bayesian inference; SciPost Foundation; SciPost Physics Core; 6; 2; 4-2023; 1-192666-9366CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://scipost.org/10.21468/SciPostPhysCore.6.2.046info:eu-repo/semantics/altIdentifier/doi/10.21468/SCIPOSTPHYSCORE.6.2.046info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:34:56Zoai:ri.conicet.gov.ar:11336/243762instacron: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:34982025-09-29 10:34:56.948CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Exploring unsupervised top tagging using Bayesian inference |
title |
Exploring unsupervised top tagging using Bayesian inference |
spellingShingle |
Exploring unsupervised top tagging using Bayesian inference Alvarez, Ezequiel Jets machine learning top quark |
title_short |
Exploring unsupervised top tagging using Bayesian inference |
title_full |
Exploring unsupervised top tagging using Bayesian inference |
title_fullStr |
Exploring unsupervised top tagging using Bayesian inference |
title_full_unstemmed |
Exploring unsupervised top tagging using Bayesian inference |
title_sort |
Exploring unsupervised top tagging using Bayesian inference |
dc.creator.none.fl_str_mv |
Alvarez, Ezequiel Szewc, Manuel Szynkman, Alejandro Andrés Tanco, Santiago Andrés Tarutina, Tatiana |
author |
Alvarez, Ezequiel |
author_facet |
Alvarez, Ezequiel Szewc, Manuel Szynkman, Alejandro Andrés Tanco, Santiago Andrés Tarutina, Tatiana |
author_role |
author |
author2 |
Szewc, Manuel Szynkman, Alejandro Andrés Tanco, Santiago Andrés Tarutina, Tatiana |
author2_role |
author author author author |
dc.subject.none.fl_str_mv |
Jets machine learning top quark |
topic |
Jets machine learning top quark |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.3 https://purl.org/becyt/ford/1 |
dc.description.none.fl_txt_mv |
Recognizing hadronically decaying top-quark jets in a sample of jets, or even its total fraction in the sample, is an important step in many LHC searches for Standard Model and Beyond Standard Model physics as well. Although there exists outstanding top-tagger algorithms, their construction and their expected performance rely on Montecarlo simulations, which may induce potential biases. For these reasons we develop two simple unsupervised top-tagger algorithms based on performing Bayesian inference on a mixture model. In one of them we use as the observed variable a new geometrically-based observable Ã3, and in the other we consider the more traditional τ3/τ2 N-subjettiness ratio, which yields a better performance. As expected, we find that the unsupervised tagger performance is below existing supervised taggers, reaching expected Area Under Curve AUC ∼ 0.80 − 0.81 and accuracies of about 69% − 75% in a full range of sample purity. However, these performances are more robust to possible biases in the Montecarlo that their supervised counterparts. Our findings are a step towards exploring and considering simpler and unbiased taggers. Fil: Alvarez, Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Szewc, Manuel. University of Cincinnati; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Szynkman, Alejandro Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; Argentina Fil: Tanco, Santiago Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; Argentina Fil: Tarutina, Tatiana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; Argentina |
description |
Recognizing hadronically decaying top-quark jets in a sample of jets, or even its total fraction in the sample, is an important step in many LHC searches for Standard Model and Beyond Standard Model physics as well. Although there exists outstanding top-tagger algorithms, their construction and their expected performance rely on Montecarlo simulations, which may induce potential biases. For these reasons we develop two simple unsupervised top-tagger algorithms based on performing Bayesian inference on a mixture model. In one of them we use as the observed variable a new geometrically-based observable Ã3, and in the other we consider the more traditional τ3/τ2 N-subjettiness ratio, which yields a better performance. As expected, we find that the unsupervised tagger performance is below existing supervised taggers, reaching expected Area Under Curve AUC ∼ 0.80 − 0.81 and accuracies of about 69% − 75% in a full range of sample purity. However, these performances are more robust to possible biases in the Montecarlo that their supervised counterparts. Our findings are a step towards exploring and considering simpler and unbiased taggers. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-04 |
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/11336/243762 Alvarez, Ezequiel; Szewc, Manuel; Szynkman, Alejandro Andrés; Tanco, Santiago Andrés; Tarutina, Tatiana; Exploring unsupervised top tagging using Bayesian inference; SciPost Foundation; SciPost Physics Core; 6; 2; 4-2023; 1-19 2666-9366 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/243762 |
identifier_str_mv |
Alvarez, Ezequiel; Szewc, Manuel; Szynkman, Alejandro Andrés; Tanco, Santiago Andrés; Tarutina, Tatiana; Exploring unsupervised top tagging using Bayesian inference; SciPost Foundation; SciPost Physics Core; 6; 2; 4-2023; 1-19 2666-9366 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/https://scipost.org/10.21468/SciPostPhysCore.6.2.046 info:eu-repo/semantics/altIdentifier/doi/10.21468/SCIPOSTPHYSCORE.6.2.046 |
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 |
SciPost Foundation |
publisher.none.fl_str_mv |
SciPost Foundation |
dc.source.none.fl_str_mv |
reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
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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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13.070432 |