Improved calorimetric particle identification in NA62 using machine learning techniques

NA62 Collaboration

doi:10.1007/JHEP11(2023)138

Improved calorimetric particle identification in NA62 using machine learning techniques

NA62 Collaboration

Physics and Astronomy

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Abstract

Measurement of the ultra-rare K⁺→ π⁺ νν̅ decay at the NA62 experiment at CERN requires high-performance particle identification to distinguish muons from pions. Calorimetric identification currently in use, based on a boosted decision tree algorithm, achieves a muon misidentification probability of 1.2 × 10⁻⁵ for a pion identification efficiency of 75% in the momentum range of 15–40 GeV/c. In this work, calorimetric identification performance is improved by developing an algorithm based on a convolutional neural network classifier augmented by a filter. Muon misidentification probability is reduced by a factor of six with respect to the current value for a fixed pion-identification efficiency of 75%. Alternatively, pion identification efficiency is improved from 72% to 91% for a fixed muon misidentification probability of 10⁻⁵.

Original language	English
Article number	138
Number of pages	14
Journal	JHEP
DOIs	https://doi.org/10.1007/JHEP11(2023)138
Publication status	Published - 21 Nov 2023

Bibliographical note

Acknowledgments:
The authors are grateful to T. Timbers and the University of British Columbia Master of Data Science Capstone team for their assistance. The authors would also like to thank W. Deng, B. Lie and S. Sethi for their contributions to an early phase of this work. It is a pleasure to express our appreciation to the staff of the CERN laboratory and the technical staff of the participating laboratories and universities for their efforts in the operation of the experiment and data processing.

The cost of the experiment and its auxiliary systems was supported by the funding agencies of the Collaboration Institutes. We are particularly indebted to: F.R.S.-FNRS (Fonds de la Recherche Scientifique — FNRS), under Grants No. 4.4512.10, 1.B.258.20, Belgium; CECI (Consortium des Equipements de Calcul Intensif), funded by the Fonds de la Recherche Scientifique de Belgique (F.R.S.-FNRS) under Grant No. 2.5020.11 and by the Walloon Region, Belgium; NSERC (Natural Sciences and Engineering Research Council), funding SAPPJ-2018-0017, Canada; MEYS (Ministry of Education, Youth and Sports) funding LM 2018104, Czech Republic; BMBF (Bundesministerium für Bildung und Forschung) contracts 05H12UM5, 05H15UMCNA and 05H18UMCNA, Germany; INFN (Istituto Nazionale di Fisica Nucleare), Italy; MIUR (Ministero dell’Istruzione, dell’Università e della Ricerca), Italy; CONACyT (Consejo Nacional de Ciencia y Tecnología), Mexico; IFA (Institute of Atomic Physics) Romanian CERN-RO No. 1/16.03.2016 and Nucleus Programme PN 19 06 01 04, Romania; MESRS (Ministry of Education, Science, Research and Sport), Slovakia; CERN (European Organization for Nuclear Research), Switzerland; STFC (Science and Technology Facilities Council), United Kingdom; NSF (National Science Foundation) Award Numbers 1506088 and 1806430, U.S.A.; ERC (European Research Council) “UniversaLepto” advanced grant 268062, “KaonLepton” starting grant 336581, Europe.

Individuals have received support from: Charles University Research Center (UNCE/SCI/013), Czech Republic; Ministero dell’Istruzione, dell’Università e della Ricerca (MIUR “Futuro in ricerca 2012” grant RBFR12JF2Z, Project GAP), Italy; the Royal Society (grants UF100308, UF0758946), United Kingdom; STFC (Rutherford fellowships ST/J00412X/1, ST/M005798/1), United Kingdom; ERC (grants 268062, 336581 and starting grant 802836 “AxScale”); EU Horizon 2020 (Marie Skłodowska-Curie grants 701386, 754496, 842407, 893101, 101023808).

Keywords

Fixed Target Experiments
Branching fraction
Rare Decay
Flavour Physics

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GilE2023ImprovedFinal published version, 505 KBLicence: Creative Commons: Attribution (CC BY)

Cite this

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title = "Improved calorimetric particle identification in NA62 using machine learning techniques",

abstract = "Measurement of the ultra-rare K+ → π+ νν̅ decay at the NA62 experiment at CERN requires high-performance particle identification to distinguish muons from pions. Calorimetric identification currently in use, based on a boosted decision tree algorithm, achieves a muon misidentification probability of 1.2 × 10−5 for a pion identification efficiency of 75% in the momentum range of 15–40 GeV/c. In this work, calorimetric identification performance is improved by developing an algorithm based on a convolutional neural network classifier augmented by a filter. Muon misidentification probability is reduced by a factor of six with respect to the current value for a fixed pion-identification efficiency of 75%. Alternatively, pion identification efficiency is improved from 72% to 91% for a fixed muon misidentification probability of 10−5.",

keywords = "Fixed Target Experiments, Branching fraction, Rare Decay, Flavour Physics",

author = "{NA62 Collaboration} and B. Velghe and E. Goudzovski and J. Henshaw and C. Kenworthy and C. Lazzeroni and A. Romano and J. Sanders and A. Sergi and A. Tomczak",

note = "Acknowledgments: The authors are grateful to T. Timbers and the University of British Columbia Master of Data Science Capstone team for their assistance. The authors would also like to thank W. Deng, B. Lie and S. Sethi for their contributions to an early phase of this work. It is a pleasure to express our appreciation to the staff of the CERN laboratory and the technical staff of the participating laboratories and universities for their efforts in the operation of the experiment and data processing. The cost of the experiment and its auxiliary systems was supported by the funding agencies of the Collaboration Institutes. We are particularly indebted to: F.R.S.-FNRS (Fonds de la Recherche Scientifique — FNRS), under Grants No. 4.4512.10, 1.B.258.20, Belgium; CECI (Consortium des Equipements de Calcul Intensif), funded by the Fonds de la Recherche Scientifique de Belgique (F.R.S.-FNRS) under Grant No. 2.5020.11 and by the Walloon Region, Belgium; NSERC (Natural Sciences and Engineering Research Council), funding SAPPJ-2018-0017, Canada; MEYS (Ministry of Education, Youth and Sports) funding LM 2018104, Czech Republic; BMBF (Bundesministerium f{\"u}r Bildung und Forschung) contracts 05H12UM5, 05H15UMCNA and 05H18UMCNA, Germany; INFN (Istituto Nazionale di Fisica Nucleare), Italy; MIUR (Ministero dell{\textquoteright}Istruzione, dell{\textquoteright}Universit{\`a} e della Ricerca), Italy; CONACyT (Consejo Nacional de Ciencia y Tecnolog{\'i}a), Mexico; IFA (Institute of Atomic Physics) Romanian CERN-RO No. 1/16.03.2016 and Nucleus Programme PN 19 06 01 04, Romania; MESRS (Ministry of Education, Science, Research and Sport), Slovakia; CERN (European Organization for Nuclear Research), Switzerland; STFC (Science and Technology Facilities Council), United Kingdom; NSF (National Science Foundation) Award Numbers 1506088 and 1806430, U.S.A.; ERC (European Research Council) “UniversaLepto” advanced grant 268062, “KaonLepton” starting grant 336581, Europe. Individuals have received support from: Charles University Research Center (UNCE/SCI/013), Czech Republic; Ministero dell{\textquoteright}Istruzione, dell{\textquoteright}Universit{\`a} e della Ricerca (MIUR “Futuro in ricerca 2012” grant RBFR12JF2Z, Project GAP), Italy; the Royal Society (grants UF100308, UF0758946), United Kingdom; STFC (Rutherford fellowships ST/J00412X/1, ST/M005798/1), United Kingdom; ERC (grants 268062, 336581 and starting grant 802836 “AxScale”); EU Horizon 2020 (Marie Sk{\l}odowska-Curie grants 701386, 754496, 842407, 893101, 101023808).",

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language = "English",

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AU - NA62 Collaboration

AU - Velghe, B.

AU - Goudzovski, E.

AU - Henshaw, J.

AU - Kenworthy, C.

AU - Lazzeroni, C.

AU - Romano, A.

AU - Sanders, J.

AU - Sergi, A.

AU - Tomczak, A.

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PY - 2023/11/21

Y1 - 2023/11/21

N2 - Measurement of the ultra-rare K+ → π+ νν̅ decay at the NA62 experiment at CERN requires high-performance particle identification to distinguish muons from pions. Calorimetric identification currently in use, based on a boosted decision tree algorithm, achieves a muon misidentification probability of 1.2 × 10−5 for a pion identification efficiency of 75% in the momentum range of 15–40 GeV/c. In this work, calorimetric identification performance is improved by developing an algorithm based on a convolutional neural network classifier augmented by a filter. Muon misidentification probability is reduced by a factor of six with respect to the current value for a fixed pion-identification efficiency of 75%. Alternatively, pion identification efficiency is improved from 72% to 91% for a fixed muon misidentification probability of 10−5.

AB - Measurement of the ultra-rare K+ → π+ νν̅ decay at the NA62 experiment at CERN requires high-performance particle identification to distinguish muons from pions. Calorimetric identification currently in use, based on a boosted decision tree algorithm, achieves a muon misidentification probability of 1.2 × 10−5 for a pion identification efficiency of 75% in the momentum range of 15–40 GeV/c. In this work, calorimetric identification performance is improved by developing an algorithm based on a convolutional neural network classifier augmented by a filter. Muon misidentification probability is reduced by a factor of six with respect to the current value for a fixed pion-identification efficiency of 75%. Alternatively, pion identification efficiency is improved from 72% to 91% for a fixed muon misidentification probability of 10−5.

KW - Fixed Target Experiments

KW - Branching fraction

KW - Rare Decay

KW - Flavour Physics

U2 - 10.1007/JHEP11(2023)138

DO - 10.1007/JHEP11(2023)138

M3 - Article

SN - 1126-6708

JO - JHEP

JF - JHEP

M1 - 138

ER -

Improved calorimetric particle identification in NA62 using machine learning techniques

Abstract

Bibliographical note

Keywords

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