Searches for the BSM scenarios at the LHC using decision tree-based machine learning algorithms: a comparative study and review of random forest, AdaBoost, XGBoost and LightGBM frameworks
Machine learning algorithms are now being extensively used in our daily lives, spanning across diverse industries as well as academia. In the field of high energy physics (HEP), the most common and challenging task is separating a rare signal from a much larger background. The boosted decision tree...
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| Vydáno v: | The European physical journal. ST, Special topics Ročník 233; číslo 15-16; s. 2425 - 2463 |
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| Jazyk: | angličtina |
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01.11.2024
Springer Nature B.V |
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| ISSN: | 1951-6355, 1951-6401 |
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| Abstract | Machine learning algorithms are now being extensively used in our daily lives, spanning across diverse industries as well as academia. In the field of high energy physics (HEP), the most common and challenging task is separating a rare signal from a much larger background. The boosted decision tree algorithm has been a cornerstone of the high energy physics for analyzing event triggering, particle identification, jet tagging, object reconstruction, event classification, and other related tasks for quite some time. This article presents a comprehensive overview of research conducted by both HEP experimental and phenomenological groups that utilize decision tree algorithms in the context of the standard model and supersymmetry (SUSY). We also summarize the basic concept of machine learning and decision tree algorithm along with the working principle of random forest, AdaBoost and two gradient boosting frameworks, such as XGBoost and LightGBM. Using a case study of electroweakino production at the high-luminosity LHC, we demonstrate how these algorithms lead to improvement in the search sensitivity compared to traditional cut-based methods in both compressed and non-compressed R-parity conserving SUSY scenarios. The effect of different hyperparameters and their optimization, and feature importance study using SHapley values are discussed in detail. |
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| AbstractList | Machine learning algorithms are now being extensively used in our daily lives, spanning across diverse industries as well as academia. In the field of high energy physics (HEP), the most common and challenging task is separating a rare signal from a much larger background. The boosted decision tree algorithm has been a cornerstone of the high energy physics for analyzing event triggering, particle identification, jet tagging, object reconstruction, event classification, and other related tasks for quite some time. This article presents a comprehensive overview of research conducted by both HEP experimental and phenomenological groups that utilize decision tree algorithms in the context of the standard model and supersymmetry (SUSY). We also summarize the basic concept of machine learning and decision tree algorithm along with the working principle of random forest, AdaBoost and two gradient boosting frameworks, such as XGBoost and LightGBM. Using a case study of electroweakino production at the high-luminosity LHC, we demonstrate how these algorithms lead to improvement in the search sensitivity compared to traditional cut-based methods in both compressed and non-compressed R-parity conserving SUSY scenarios. The effect of different hyperparameters and their optimization, and feature importance study using SHapley values are discussed in detail. |
| Author | Sarkar, Subhadeep Mondal, Arpita Choudhury, Arghya |
| Author_xml | – sequence: 1 givenname: Arghya orcidid: 0000-0001-9792-5496 surname: Choudhury fullname: Choudhury, Arghya email: arghya@iitp.ac.in organization: Department of Physics, Indian Institute of Technology Patna – sequence: 2 givenname: Arpita surname: Mondal fullname: Mondal, Arpita organization: Department of Physics, Indian Institute of Technology Patna – sequence: 3 givenname: Subhadeep surname: Sarkar fullname: Sarkar, Subhadeep organization: Department of Physics, Indian Institute of Technology Patna |
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| Cites_doi | 10.1007/JHEP11(2015)050 10.1007/JHEP06(2024)176 10.1007/JHEP08(2022)124 10.1214/aos/1016218223 10.1140/epjc/s10052-019-7450-8 10.1140/epjc/s10052-023-11532-9 10.1007/JHEP09(2017)026 10.1007/JHEP02(2021)160 10.1007/JHEP12(2023)148 10.1140/epjp/i2019-12566-5 10.1146/annurev.nucl.012809.104427 10.1007/JHEP07(2020)126 10.1007/JHEP02(2021)143 10.1016/j.nuclphysb.2014.08.012 10.1088/1126-6708/2008/12/024 10.1103/PhysRevD.97.072016 10.1142/9789812839657_0001 10.1613/jair.1199 10.1007/JHEP09(2017)064 10.1016/B978-0-08-051054-5.50005-4 10.1016/j.physletb.2021.136480 10.1111/j.1365-2966.2007.12353.x 10.1016/j.envsoft.2024.105971 10.1088/0253-6102/71/8/955 10.1007/s11433-022-1927-9 10.1140/epjc/s10052-017-5167-0 10.1016/S0020-7373(87)80053-6 10.1103/PhysRevD.70.015007 10.1140/epjc/s10052-018-5697-0 10.1111/j.1365-2966.2009.14548.x 10.1007/JHEP07(2021)167 10.1088/1126-6708/2008/04/081 10.1006/jcss.1997.1504 10.1140/epjc/s10052-019-7437-5 10.1103/PhysRevD.93.094034 10.1016/j.nima.2005.09.022 10.1017/CBO9780511617270 10.1103/PhysRevD.94.055024 10.1007/JHEP07(2016)069 10.1088/1126-6708/2006/05/026 10.1016/j.patcog.2019.02.023 10.1103/PhysRevD.92.093007 10.1142/S0217751X19300199 10.1140/epjc/s10052-021-09761-x 10.1007/978-1-4613-8716-9_14 10.1023/A:1010933404324 10.1103/PhysRevLett.98.181802 10.1117/1.2819119 10.1007/JHEP09(2018)139 10.1088/1748-0221/8/02/P02013 10.1007/JHEP07(2019)135 10.1016/j.physletb.2012.08.021 10.1103/PhysRevD.89.037702 10.1140/epjc/s10052-019-6847-8 10.1007/JHEP03(2024)144 10.1007/978-3-642-04962-0_53 10.1007/JHEP01(2019)103 10.1140/epjc/s10052-017-4814-9 10.1016/j.nuclphysb.2019.114613 10.1140/epjc/s10052-011-1554-0 10.1088/1748-0221/13/05/P05011 10.1140/epjc/s10052-021-09900-4 10.1142/S0217751X22501974 10.1142/S0217751X20020030 10.1007/s41781-017-0002-8 10.1007/JHEP07(2015)038 10.1088/1742-6596/1085/4/042042 10.1007/JHEP02(2014)057 10.1103/PhysRevD.104.032006 10.1007/JHEP01(2017)110 10.1145/307400.307419 10.1007/s41781-018-0015-y 10.1007/JHEP07(2023)215 10.1088/1126-6708/2000/05/021 10.1007/JHEP04(2022)015 10.1145/3065386 10.1007/JHEP11(2018)085 10.1103/PhysRevD.78.012005 10.1140/epjc/s10052-023-11216-4 10.1007/JHEP07(2014)079 10.1007/JHEP05(2021)252 10.1103/PhysRevD.55.7020 10.1016/j.physrep.2005.08.006 10.1103/PhysRevD.105.095039 10.1201/9781315139470 10.1103/PhysRevD.82.112005 10.1088/1742-6596/664/8/082025 10.1007/JHEP05(2019)181 10.1016/j.physletb.2020.136041 10.1103/PhysRevD.100.115022 10.1103/PhysRevD.103.015003 10.1016/j.physletb.2012.08.020 10.1007/JHEP02(2019)066 10.1007/JHEP08(2019)110 10.1007/JHEP07(2021)075 10.1140/epjc/s10052-020-08509-3 10.1103/PhysRevD.96.095033 10.1103/PhysRevD.93.075035 10.1007/JHEP02(2024)004 10.1103/PhysRevD.96.075046 10.1103/PhysRevD.108.115010 10.1016/j.cpc.2011.12.026 10.1140/epjst/e2020-000198-1 10.1016/j.physletb.2018.06.028 10.5121/ijdkp.2015.5201 10.1007/JHEP06(2015)020 10.1103/PhysRevD.98.075022 10.1140/epjc/s10052-021-09749-7 10.1016/j.nima.2004.12.018 10.1103/PhysRevD.84.095006 10.1007/JHEP03(2021)211 10.1140/epjc/s10052-019-6800-x 10.1371/journal.pone.0118432 10.1007/JHEP09(2013)119 10.1007/JHEP07(2019)142 10.1103/PhysRevD.83.115019 10.1103/PhysRevD.95.075025 10.1007/JHEP02(2015)118 10.1146/annurev-nucl-101917-021019 10.1007/JHEP03(2011)012 10.1007/JHEP10(2021)063 10.1007/JHEP09(2021)080 10.1103/PhysRevD.102.015031 10.1088/1748-0221/15/12/P12012 10.1515/9781400881970-018 10.1103/PhysRevD.109.035001 10.1103/PhysRevLett.131.011802 10.1007/JHEP07(2014)019 10.1007/978-0-387-84858-7 10.1007/s10462-020-09896-5 10.1016/j.phpro.2012.03.160 10.1007/0-387-25465-X_40 10.1007/JHEP04(2021)174 10.1007/JHEP06(2012)006 10.1007/JHEP06(2023)031 10.1103/PhysRevD.108.035043 10.1007/JHEP05(2017)072 10.1140/epjc/s10052-013-2677-2 10.1007/JHEP01(2016)051 10.1140/epjc/s10052-014-3076-z 10.21468/SciPostPhys.7.6.075 10.1103/PhysRevD.69.093002 10.1140/epjc/s10052-017-5196-8 10.1140/epjc/s10052-013-2650-0 10.1142/S0218001406005137 10.1007/JHEP06(2023)060 10.1103/RevModPhys.91.045002 10.1140/epjc/s10052-013-2480-0 |
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| References | BhattacherjeeBChakrabortyAChoudhuryAStatus of the MSSM Higgs sector using global analysis and direct search bounds, and future prospects at the high luminosity LHCPhys. Rev. D2015922015PhRvD..92i3007B10.1103/PhysRevD.92.093007arXiv:1504.04308 RenJWuLYangJMZhaoJExploring supersymmetry with machine learningNucl. Phys. B201994310.1016/j.nuclphysb.2019.114613arXiv:1708.06615 BhattacherjeeBSolankiPSearch for electroweakinos in R-parity violating SUSY with long-lived particles at HL-LHCJHEP2023121482023JHEP...12..148B10.1007/JHEP12(2023)148arXiv:2308.05804 SunYTodorovicSLiJReducing the overfitting of adaboost by controlling its data distribution skewnessInt. J. Pattern Recognit. Artif. Intell.2006201093111610.1142/S0218001406005137 H.-J. Yang, B.P. Roe, J. Zhu, Studies of boosted decision trees for miniboone particle identification. Nucl. Instrum. Methods Phys. Res. Sect. A Accel. Spectrom. Detect. Assoc. Equip. 555, 370–385 (2005). https://doi.org/10.1016/j.nima.2005.09.022 CerianiLVermePThe origins of the gini index: extracts from variabilit e mutabilit, by corrado giniJ. Econ. Inequal.1912102012421443 D0 Collaboration, V.M. Abazov et al., Evidence for production of single top quarks. Phys. Rev. D 78, 012005 (2008). https://doi.org/10.1103/PhysRevD.78.012005. arXiv:0803.0739 BarmanRKBhattacherjeeBChakrabortyIChoudhuryAKhanNElectroweakino searches at the HL-LHC in the baryon number violating MSSMPhys. Rev. D20211032021PhRvD.103a5003B10.1103/PhysRevD.103.015003arXiv:2003.10920 LikhomanenkoTIltenPKhairullinERogozhnikovAUstyuzhaninAWilliamsMLHCb topological trigger reoptimizationJ. Phys. Conf. Ser.201566410.1088/1742-6596/664/8/082025arXiv:1510.00572 Cms phase 1 heavy flavour identification performance and developments, cms detector performance note, cms-dp-2017-013 (2017) DjouadiAMaianiLMoreauGPolosaAQuevillonJRiquerVThe post-Higgs MSSM scenario: Habemus MSSM?Eur. Phys. J. C20137326502013EPJC...73.2650D10.1140/epjc/s10052-013-2650-0arXiv:1307.5205 DíazMARiveraMRojasNOn neutrino masses in the MSSM with BRpVNucl. Phys. B20148873383572014NuPhB.887..338D10.1016/j.nuclphysb.2014.08.012arXiv:1401.7357 TrifinopoulosSB-physics anomalies: the bridge between R -parity violating supersymmetry and flavored dark matterPhys. Rev. D20191002019PhRvD.100k5022T10.1103/PhysRevD.100.115022arXiv:1904.12940 ATLAS Collaboration, M. Aaboud et al., Search for the standard model Higgs boson produced in association with top quarks and decaying into a bb¯\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$b\bar{b}$$\end{document} pair in pp\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$pp$$\end{document} collisions at s\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sqrt{s}$$\end{document} = 13 TeV with the ATLAS detector. Phys. Rev. D 97, 072016 (2018). https://doi.org/10.1103/PhysRevD.97.072016. arXiv:1712.08895 ATLAS Collaboration, G. Aad et al., Search for direct pair production of sleptons and charginos decaying to two leptons and neutralinos with mass splittings near the W-boson mass in s\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sqrt{s}$$\end{document} = 13 TeV pp collisions with the ATLAS detector. JHEP 06, 031 (2023). https://doi.org/10.1007/JHEP06(2023)031. arXiv:2209.13935 A. Choudhury, A. Datta, Many faces of low mass neutralino dark matter in the unconstrained MSSM. LHC data and new signals. JHEP 06, 006 (2012). https://doi.org/10.1007/JHEP06(2012)006. arXiv:1203.4106 FerozFHobsonMPBridgesMMultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physicsMon. Not. R. Astron. Soc.2009398160116142009MNRAS.398.1601F10.1111/j.1365-2966.2009.14548.xarXiv:0809.3437 CMS Collaboration, A.M. Sirunyan et al., Search for charged Higgs bosons decaying into a top and a bottom quark in the all-jet final state of pp collisions at s\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sqrt{s}$$\end{document} = 13 TeV. JHEP 07, 126 (2020). https://doi.org/10.1007/JHEP07(2020)126. arXiv:2001.07763 R. Di Sipio, M. Faucci Giannelli, S. Ketabchi Haghighat, S. Palazzo, DijetGAN: a generative-adversarial network approach for the simulation of QCD Dijet events at the LHC. JHEP 08, 110 (2019). https://doi.org/10.1007/JHEP08(2019)110. arXiv:1903.02433 H.K. Dreiner, Y.S. Koay, D. Köhler, V.M. Lozano, J. Montejo Berlingen, S. Nangia et al., The ABC of RPV: classification of R-parity violating signatures at the LHC for small couplings. JHEP 07, 215 (2023). https://doi.org/10.1007/JHEP07(2023)215. arXiv:2306.07317 C. Bishop, Pattern recognition and machine learning 16, 140–155. 01 (2006). https://doi.org/10.1117/1.2819119 KronheimBSKucheraMPProsperHBKarboABayesian neural networks for fast SUSY predictionsPhys. Lett. B202181310.1016/j.physletb.2020.136041arXiv:2007.04506 CollaborationCMSChatrchyanSObservation of a new boson at a mass of 125 GeV with the CMS experiment at the LHCPhys. Lett. B201271630612012PhLB..716...30C10.1016/j.physletb.2012.08.021arXiv:1207.7235 ATLAS Collaboration, M. Aaboud et al., Performance of top-quark and W\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$W$$\end{document}-boson tagging with ATLAS in Run 2 of the LHC. Eur. Phys. J. C 79, 375 (2019). https://doi.org/10.1140/epjc/s10052-019-6847-8. arXiv:1808.07858 R.K. Barman, G. Bélanger, B. Bhattacherjee, R. Godbole, R. Sengupta, Current status of the light neutralino thermal dark matter in the phenomenological MSSM. arXiv:2402.07991 CMS Collaboration, A.M. Sirunyan et al., Search for R\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R$$\end{document}-parity violating supersymmetry in pp collisions at s=\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sqrt{s} =$$\end{document} 13 TeV using b jets in a final state with a single lepton, many jets, and high sum of large-radius jet masses. Phys. Lett. B 783, 114–139 (2018). https://doi.org/10.1016/j.physletb.2018.06.028. arXiv:1712.08920 Preface to special issue on “learning to discover”. Int. J. Mod. Phys. A 35, 2002003 (2020). https://doi.org/10.1142/S0217751X20020030 M.D. Schwartz, Modern machine learning and particle physics. arXiv:2103.12226 WeissGMProvostFLearning when training data are costly: the effect of class distribution on tree inductionJ. Artif. Intell. Res.20031931535410.1613/jair.1199 QuinlanJSimplifying decision treesInt. J. Man Mach. Stud.19872722123410.1016/S0020-7373(87)80053-6 GAMBIT Collaboration, P. Athron et al., Global fits of GUT-scale SUSY models with GAMBIT. Eur. Phys. J. C 77, 824 (2017). https://doi.org/10.1140/epjc/s10052-017-5167-0. arXiv:1705.07935 BagnaschiELikelihood analysis of the pMSSM11 in light of LHC 13-TeV dataEur. Phys. J. C2018782562018EPJC...78..256B10.1140/epjc/s10052-018-5697-0arXiv:1710.11091 ATLAS Collaboration, G. Aad et al., Search for supersymmetry in events with four or more charged leptons in 139 fb-1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text{ fb}^{-1}$$\end{document} of s\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sqrt{s}$$\end{document} = 13 TeV pp collisions with the ATLAS detector, JHEP 07, 167 (2021). https://doi.org/10.1007/JHEP07(2021)167. arXiv:2103.11684 WangRAdaboost for feature selection, classification and its relation with svm, a reviewPhys. Procedia2012258008072012PhPro..25..800W10.1016/j.phpro.2012.03.160 M. Chakraborti, U. Chattopadhyay, S. Poddar, How light a Higgsino or a wino dark matter can become in a compressed scenario of MSSM. JHEP 09, 064 (2017). https://doi.org/10.1007/JHEP09(2017)064. arXiv:1702.03954 FriedmanJHastieTTibshiraniRAdditive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors)Ann. Stat.20002833740710.1214/aos/1016218223 KrizhevskyASutskeverIHintonGEImagenet classification with deep convolutional neural networksCommun. ACM201260849010.1145/3065386 GrossmanYRakshitSNeutrino masses in R-parity violating supersymmetric modelsPhys. Rev. D2004692004PhRvD..69i3002G10.1103/PhysRevD.69.093002arXiv:hep-ph/0311310 M. Endo, K. Hamaguchi, S. Iwamoto, T. Kitahara, Supersymmetric interpretation of the muon g - 2 anomaly. JHEP 07, 075 (2021). https://doi.org/10.1007/JHEP07(2021)075. arXiv:2104.03217 CollaborationCMSChatrchyanSSearch for top-squark pair production in the single-lepton final state in pp collisions at s\documentclass J Cogan (1308_CR100) 2015; 02 B Bhattacherjee (1308_CR161) 2023; 12 1308_CR75 PC Bhat (1308_CR44) 2011; 61 T Hastie (1308_CR57) 2009 A Mullin (1308_CR91) 2021; 02 D Das (1308_CR151) 2017; 96 1308_CR79 Y Sun (1308_CR175) 2006; 20 1308_CR78 Y Freund (1308_CR172) 1999; 14 E Bols (1308_CR107) 2020; 15 1308_CR176 1308_CR83 AS Cornell (1308_CR54) 2022; 04 GM Weiss (1308_CR62) 2003; 19 A Chakraborty (1308_CR104) 2019; 07 1308_CR170 PT Komiske (1308_CR103) 2017; 01 F Domingo (1308_CR150) 2019; 02 CMS Collaboration (1308_CR74) 2018; 1085 T Likhomanenko (1308_CR77) 2015; 664 Q Gu (1308_CR67) 2009 A Chakraborty (1308_CR146) 2016; 93 D Bourilkov (1308_CR46) 2020; 34 1308_CR84 1308_CR85 A Hammad (1308_CR52) 2024; 06 F Jorge (1308_CR123) 2022; 37 1308_CR163 1308_CR164 1308_CR93 RS Hundi (1308_CR148) 2011; 83 1308_CR167 1308_CR92 A Buckley (1308_CR89) 2012; 183 S Davidson (1308_CR142) 2000; 05 D Guest (1308_CR45) 2018; 68 A Choudhury (1308_CR86) 2024; 02 1308_CR160 1308_CR17 E Bagnaschi (1308_CR134) 2018; 78 1308_CR18 1308_CR15 1308_CR16 BS Kronheim (1308_CR90) 2021; 813 1308_CR19 1308_CR10 1308_CR98 1308_CR115 J Friedman (1308_CR168) 2000; 28 1308_CR117 1308_CR13 1308_CR118 1308_CR14 M Abdughani (1308_CR50) 2019; 71 1308_CR119 H Baer (1308_CR3) 2006 1308_CR11 S Caron (1308_CR87) 2017; 77 1308_CR99 1308_CR12 1308_CR110 1308_CR112 1308_CR113 L Ceriani (1308_CR165) 1912; 10 P Musella (1308_CR95) 2018; 2 R Trotta (1308_CR82) 2008; 12 C Bentéjac (1308_CR61) 2021; 54 M Bridges (1308_CR88) 2011; 03 1308_CR28 RK Barman (1308_CR136) 2019; 134 1308_CR29 1308_CR26 VV Gligorov (1308_CR76) 2013; 8 R Wang (1308_CR173) 2012; 25 1308_CR27 Y Freund (1308_CR174) 1997; 55 1308_CR20 W Altmannshofer (1308_CR147) 2020; 102 1308_CR21 A Choudhury (1308_CR178) 2013; 09 1308_CR105 1308_CR106 1308_CR24 1308_CR25 1308_CR108 1308_CR22 1308_CR109 1308_CR23 J Quinlan (1308_CR166) 1987; 27 1308_CR183 L Breiman (1308_CR171) 2001; 45 1308_CR186 1308_CR187 1308_CR188 CDF Collaboration (1308_CR36) 2010; 82 S Roy (1308_CR143) 1997; 55 CMS Collaboration (1308_CR122) 2013; 73 T Keck (1308_CR139) 2017; 1 1308_CR181 J Lin (1308_CR94) 2019; 05 1308_CR182 1308_CR37 H-J Yang (1308_CR169) 2005; 555 1308_CR38 1308_CR31 1308_CR32 1308_CR137 1308_CR138 1308_CR30 1308_CR35 1308_CR33 1308_CR34 J Ren (1308_CR96) 2019; 943 1308_CR130 1308_CR131 A Djouadi (1308_CR133) 2013; 73 P Baldi (1308_CR102) 2016; 93 1308_CR132 G Ke (1308_CR177) 2017; 30 S Trifinopoulos (1308_CR149) 2019; 100 BC Allanach (1308_CR144) 2008; 04 A Datta (1308_CR180) 2019; 01 1308_CR49 M Drees (1308_CR2) 2004 1308_CR42 1308_CR125 1308_CR43 1308_CR126 J Alwall (1308_CR184) 2014; 07 1308_CR40 1308_CR127 1308_CR41 1308_CR128 1308_CR1 1308_CR47 1308_CR5 1308_CR4 1308_CR7 1308_CR6 1308_CR120 1308_CR9 A Choudhury (1308_CR159) 2024; 109 L Breiman (1308_CR162) 1984 1308_CR8 1308_CR124 Y Grossman (1308_CR141) 2004; 69 MA Díaz (1308_CR145) 2014; 887 F Feroz (1308_CR81) 2009; 398 C Balazs (1308_CR121) 2004; 70 1308_CR59 F Feroz (1308_CR80) 2008; 384 M Niazkar (1308_CR60) 2024; 174 D Alvestad (1308_CR129) 2023; 83 1308_CR53 1308_CR158 1308_CR58 1308_CR55 A Krizhevsky (1308_CR111) 2012; 60 1308_CR56 S Caron (1308_CR97) 2019; 79 1308_CR152 1308_CR153 1308_CR154 L de Oliveira (1308_CR101) 2016; 07 1308_CR155 T Saito (1308_CR65) 2015 1308_CR156 1308_CR157 CMS Collaboration (1308_CR114) 2012; 716 RK Barman (1308_CR179) 2021; 103 R Barbier (1308_CR116) 2005; 420 Y Freund (1308_CR73) 1997; 55 G Cowan (1308_CR69) 2011; 71 1308_CR64 1308_CR63 B Bhattacherjee (1308_CR135) 2015; 92 H Baer (1308_CR140) 2022; 105 1308_CR68 1308_CR66 1308_CR71 1308_CR72 G Carleo (1308_CR48) 2019; 91 1308_CR70 A Hammad (1308_CR51) 2024; 03 CMS Collaboration (1308_CR39) 2014; 74 B Fuks (1308_CR185) 2013; 73 |
| References_xml | – reference: J. Bardhan, T. Mandal, S. Mitra, C. Neeraj, M. Patra, Unsupervised and lightly supervised learning in particle physics. arXiv:2403.13676 – reference: Y. Coadou, Boosted decision trees. arXiv:2206.09645 – reference: GligorovVVWilliamsMEfficient, reliable and fast high-level triggering using a bonsai boosted decision treeJINST20138P020132013JInst...8P2013G10.1088/1748-0221/8/02/P02013arXiv:1210.6861 – reference: S.M. Lundberg, G.G. Erion, S.-I. Lee, Consistent individualized feature attribution for tree ensembles. arXiv:1802.03888 (2018) – reference: SunYTodorovicSLiJReducing the overfitting of adaboost by controlling its data distribution skewnessInt. J. Pattern Recognit. Artif. Intell.2006201093111610.1142/S0218001406005137 – reference: CollaborationCMSKhachatryanVObservation of the diphoton decay of the Higgs Boson and measurement of its propertiesEur. Phys. J. C201474307610.1140/epjc/s10052-014-3076-zarXiv:1407.0558 – reference: FerozFHobsonMPBridgesMMultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physicsMon. Not. R. Astron. Soc.2009398160116142009MNRAS.398.1601F10.1111/j.1365-2966.2009.14548.xarXiv:0809.3437 – reference: G.H.Y. LeCun, Y. Bengio, Deep learning – reference: B. Bhattacherjee, P. Konar, V.S. Ngairangbam, P. Solanki, LLPNet: graph autoencoder for triggering light long-lived particles at HL-LHC. arXiv:2308.13611 – reference: HundiRSConstraints from neutrino masses and muon (g-2) in the bilinear R-parity violating supersymmetric modelPhys. Rev. D2011832011PhRvD..83k5019H10.1103/PhysRevD.83.115019arXiv:1101.2810 – reference: J. Dutta, P. Konar, S. Mondal, B. Mukhopadhyaya, S.K. Rai, Search for a compressed supersymmetric spectrum with a light Gravitino. JHEP 09, 026 (2017). https://doi.org/10.1007/JHEP09(2017)026. arXiv:1704.04617 – reference: Cms susy public result. https://cms-results.web.cern.ch/cms-results/public-results/preliminary-results/SUS/index.html – reference: CMS Collaboration, A. Zabi, J.W. Berryhill, E. Perez, A.D. Tapper, The phase-2 upgrade of the CMS level-1 trigger – reference: BalazsCCarenaMWagnerCEMDark matter, light stops and electroweak baryogenesisPhys. Rev. D2004702004PhRvD..70a5007B10.1103/PhysRevD.70.015007arXiv:hep-ph/0403224 – reference: Heavy flavor identification at cms with deep neural networks (2020) – reference: ATLAS Collaboration, M. Aaboud et al., Search for the standard model Higgs boson produced in association with top quarks and decaying into a bb¯\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$b\bar{b}$$\end{document} pair in pp\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$pp$$\end{document} collisions at s\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sqrt{s}$$\end{document} = 13 TeV with the ATLAS detector. Phys. Rev. D 97, 072016 (2018). https://doi.org/10.1103/PhysRevD.97.072016. arXiv:1712.08895 – reference: BaerHKaoCBargerVJainRSenguptaDTataXDetecting heavy Higgs bosons from natural SUSY at a 100 TeV hadron colliderPhys. Rev. D20221052022PhRvD.105i5039B10.1103/PhysRevD.105.095039arXiv:2112.02232 – reference: CaronSHeskesTOttenSStienenBConstraining the parameters of high-dimensional models with active learningEur. Phys. J. C2019799442019EPJC...79..944C10.1140/epjc/s10052-019-7437-5arXiv:1905.08628 – reference: CMS Collaboration, A.M. Sirunyan et al., Search for charged Higgs bosons decaying into a top and a bottom quark in the all-jet final state of pp collisions at s\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sqrt{s}$$\end{document} = 13 TeV. JHEP 07, 126 (2020). https://doi.org/10.1007/JHEP07(2020)126. arXiv:2001.07763 – reference: A. Choudhury, L. Darme, L. Roszkowski, E.M. Sessolo, S. Trojanowski, Muon g -\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-$$\end{document} 2 and related phenomenology in constrained vector-like extensions of the MSSM. JHEP 05, 072 (2017). https://doi.org/10.1007/JHEP05(2017)072. arXiv:1701.08778 – reference: CollaborationCMSChatrchyanSSearch for top-squark pair production in the single-lepton final state in pp collisions at s\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sqrt{s}$$\end{document} = 8 TeVEur. Phys. J. C20137326772013EPJC...73.2677C10.1140/epjc/s10052-013-2677-2arXiv:1308.1586 – reference: J. Bendavid, Efficient Monte Carlo integration using boosted decision trees and generative deep neural networks. arXiv:1707.00028 – reference: ATLAS Collaboration, M. Aaboud et al., Performance of top-quark and W\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$W$$\end{document}-boson tagging with ATLAS in Run 2 of the LHC. Eur. Phys. J. C 79, 375 (2019). https://doi.org/10.1140/epjc/s10052-019-6847-8. arXiv:1808.07858 – reference: BourilkovDMachine and deep learning applications in particle physicsInt. J. Mod. Phys. A20203419300192019IJMPA..3430019B10.1142/S0217751X19300199arXiv:1912.08245 – reference: TrottaRFerozFHobsonMPRoszkowskiLRuiz de AustriRThe Impact of priors and observables on parameter inferences in the constrained MSSMJHEP2008120242008JHEP...12..024T10.1088/1126-6708/2008/12/024arXiv:0809.3792 – reference: KeckTFastBDT: a speed-optimized multivariate classification algorithm for the Belle II experimentComput. Softw. Big Sci.20171210.1007/s41781-017-0002-8 – reference: ATLAS Collaboration, M. Aaboud et al., Search for charged Higgs bosons decaying via H±→τ±ντ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$H^{\pm } \rightarrow \tau ^{\pm }\nu _{\tau }$$\end{document} in the τ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau$$\end{document}+jets and τ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau$$\end{document}+lepton final states with 36 fb-1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text{ fb}^{-1}$$\end{document} of pp\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$pp$$\end{document} collision data recorded at s=13\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sqrt{s} = 13$$\end{document} TeV with the ATLAS experiment. JHEP 09, 139 (2018). https://doi.org/10.1007/JHEP09(2018)139. arXiv:1807.07915 – reference: ATLAS Collaboration, M. Aaboud et al., Search for charged Higgs bosons decaying into top and bottom quarks at s\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sqrt{s}$$\end{document} = 13 TeV with the ATLAS detector. JHEP 11, 085 (2018). https://doi.org/10.1007/JHEP11(2018)085. arXiv:1808.03599 – reference: C. gini, variabilità e mutabilità, (reprinted in memorie di metodologica statistica, eds. e. pizetti and t. salvemini, libreria eredi virgilio veschi, rome, 1955) (1912) – reference: M. Chakraborti, U. Chattopadhyay, A. Choudhury, A. Datta, S. Poddar, Reduced LHC constraints for Higgsino-like heavier electroweakinos. JHEP 11, 050 (2015). https://doi.org/10.1007/JHEP11(2015)050. arXiv:1507.01395 – reference: M. Chakraborti, U. Chattopadhyay, A. Choudhury, A. Datta, S. Poddar, The electroweak sector of the pMSSM in the light of LHC - 8 TeV and other data. JHEP 07, 019 (2014). https://doi.org/10.1007/JHEP07(2014)019. arXiv:1404.4841 – reference: GuestDCranmerKWhitesonDDeep learning and its application to LHC physicsAnn. Rev. Nucl. Part. Sci.2018681611812018ARNPS..68..161G10.1146/annurev-nucl-101917-021019arXiv:1806.11484 – reference: CaronSKimJSRolbieckiKRuiz de AustriRStienenBThe BSM-AI project: SUSY-AI-generalizing LHC limits on supersymmetry with machine learningEur. Phys. J. C2017772572017EPJC...77..257C10.1140/epjc/s10052-017-4814-9arXiv:1605.02797 – reference: N. Bhattacharyya, A. Choudhury, A. Datta, Low mass neutralino dark matter in mSUGRA and more general models in the light of LHC data. Phys. Rev. D 84, 095006 (2011). https://doi.org/10.1103/PhysRevD.84.095006. arXiv:1107.1997 – reference: ATLAS Collaboration, G. Aad et al., Evidence for tt¯tt¯\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$t\bar{t}t\bar{t}$$\end{document} production in the multilepton final state in proton–proton collisions at s=13\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sqrt{s}=13$$\end{document}TeV\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text{ TeV }$$\end{document} with the ATLAS detector. Eur. Phys. J. C 80, 1085 (2020). https://doi.org/10.1140/epjc/s10052-020-08509-3. arXiv:2007.14858 – reference: FreundYSchapireRAbeNA short introduction to boostingJ. Jpn. Soc. Artif. Intell.1999141612 – reference: BhattacherjeeBSolankiPSearch for electroweakinos in R-parity violating SUSY with long-lived particles at HL-LHCJHEP2023121482023JHEP...12..148B10.1007/JHEP12(2023)148arXiv:2308.05804 – reference: F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel et al., Scikit-learn: machine learning in python (2018) – reference: de OliveiraLKaganMMackeyLNachmanBSchwartzmanAJet-images—deep learning editionJHEP20160706910.1007/JHEP07(2016)069arXiv:1511.05190 – reference: M. Frank, Y. Hicylmaz, S. Mondal, O. Ozdal, C.S. Un, Electron and muon magnetic moments and implications for dark matter and model characterisation in non-universal U(1) supersymmetric models. JHEP 10, 063 (2021). https://doi.org/10.1007/JHEP10(2021)063. arXiv:2107.04116 – reference: MullinANichollsSPaceyHParkerMWhiteMWilliamsSDoes SUSY have friends? A new approach for LHC event analysisJHEP2021021602021JHEP...02..160M10.1007/JHEP02(2021)160arXiv:1912.10625 – reference: R. Kumar Barman, G. Belanger, R.M. Godbole, Status of low mass LSP in SUSY, Eur. Phys. J. ST 229, 3159–3185 (2020). https://doi.org/10.1140/epjst/e2020-000198-1. arXiv:2010.11674 – reference: L.S. Shapley, 17. A Value for n-Person Games (Princeton University Press, Princeton, 1953), pp. 307–318. https://doi.org/10.1515/9781400881970-018 – reference: Atlas susy public result. https://twiki.cern.ch/twiki/bin/view/AtlasPublic/SupersymmetryPublicResults – reference: N.V. Chawla, Data Mining for Imbalanced Datasets: An Overview (Springer US, Boston, 2005), pp.853–867. https://doi.org/10.1007/0-387-25465-X_40 – reference: DreesMRoyPGodboleRTheory and Phenomenology of Sparticles: An Account of Four-Dimensional N2004SingaporeWorld Scientific – reference: H. Banerjee, B. Dutta, S. Roy, Supersymmetric gauged U(1)Lμ-Lτ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\rm U {(1)}_{L_{\mu }-{L}_{\tau }}$$\end{document} model for electron and muon (g-2)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(g - 2)$$\end{document} anomaly. JHEP 03, 211 (2021). https://doi.org/10.1007/JHEP03(2021)211. arXiv:2011.05083 – reference: AlvestadDFominNKerstenJMaelandSStrümkeIBeyond cuts in small signal scenarios: enhanced sneutrino detectability using machine learningEur. Phys. J. C2023833792023EPJC...83..379A10.1140/epjc/s10052-023-11532-9arXiv:2108.03125 – reference: CornellASDoorsamyWFuksBHarmsenGMasonLBoosted decision trees in the era of new physics: a smuon analysis case studyJHEP2022040152022JHEP...04..015C10.1007/JHEP04(2022)015arXiv:2109.11815 – reference: JorgeFRonaldRJesusSJuanMCarlosATop squark signal significance enhancement by different machine learning algorithmsInt. J. Mod. Phys. A202237225019710.1142/S0217751X22501974arXiv:2106.06813 – reference: M. Chakraborti, S. Iwamoto, J.S. Kim, R. Masełek, K. Sakurai, Supersymmetric explanation of the muon g - 2 anomaly with and without stable neutralino. JHEP 08, 124 (2022). https://doi.org/10.1007/JHEP08(2022)124. arXiv:2202.12928 – reference: P. Athron, C. Balazs, D.H.J. Jacob, W. Kotlarski, D. Stockinger, H. Stockinger-Kim, New physics explanations of aμ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text{a}_{\mu }$$\end{document} in light of the FNAL muon g - 2 measurement. JHEP 09, 080 (2021). https://doi.org/10.1007/JHEP09(2021)080. arXiv:2104.03691 – reference: ATLAS Collaboration, G. Aad et al., Search for direct pair production of sleptons and charginos decaying to two leptons and neutralinos with mass splittings near the W-boson mass in s\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sqrt{s}$$\end{document} = 13 TeV pp collisions with the ATLAS detector. JHEP 06, 031 (2023). https://doi.org/10.1007/JHEP06(2023)031. arXiv:2209.13935 – reference: CarleoGCiracICranmerKDaudetLSchuldMTishbyNMachine learning and the physical sciencesRev. Mod. Phys.2019912019RvMP...91d5002C10.1103/RevModPhys.91.045002arXiv:1903.10563 – reference: KrizhevskyASutskeverIHintonGEImagenet classification with deep convolutional neural networksCommun. ACM201260849010.1145/3065386 – reference: R. Baruah, S. Mondal, S.K. Patra, S. Roy, Probing intractable beyond-standard-model parameter spaces armed with machine learning. arXiv:2404.02698 – reference: ChoudhuryAMondalAMondalSSarkarSImproving sensitivity of trilinear R-parity violating SUSY searches using machine learning at the LHCPhys. Rev. D20241092024PhRvD.109c5001C10.1103/PhysRevD.109.035001arXiv:2308.02697 – reference: CMS Collaboration, A. Tumasyan et al., Search for top squarks in the four-body decay mode with single lepton final states in proton-proton collisions at s\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sqrt{s}$$\end{document} = 13 TeV. JHEP 06, 060 (2023). https://doi.org/10.1007/JHEP06(2023)060. arXiv:2301.08096 – reference: DomingoFDreinerHKKimJSKraussMELozanoMWangZSUpdating bounds on R\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R$$\end{document}-parity violating supersymmetry from meson oscillation dataJHEP2019020662019JHEP...02..066D393313310.1007/JHEP02(2019)066arXiv:1810.08228 – reference: F. Feroz, M.P. Hobson, M. Bridges, MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physics. Mon. Not. R. Astron. Soc. 398(09), 1601–1614 (2009). https://doi.org/10.1111/j.1365-2966.2009.14548.x. https://academic.oup.com/mnras/article-pdf/398/4/1601/3039078/mnras0398-1601.pdf – reference: G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma et al., Lightgbm: a highly efficient gradient boosting decision tree. In Advances in Neural Information Processing Systems, ed. by I. Guyon, U.V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan et al., vol. 30 (Curran Associates, Inc., 2017) – reference: DjouadiAMaianiLMoreauGPolosaAQuevillonJRiquerVThe post-Higgs MSSM scenario: Habemus MSSM?Eur. Phys. J. C20137326502013EPJC...73.2650D10.1140/epjc/s10052-013-2650-0arXiv:1307.5205 – reference: J. Cao, F. Li, J. Lian, Y. Pan, D. Zhang, Impact of LHC probes of SUSY and recent measurement of (g -\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-$$\end{document} 2) on Z3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbb{Z} }_{3}$$\end{document}-NMSSM. Sci. China Phys. Mech. Astron. 65, 291012 (2022). https://doi.org/10.1007/s11433-022-1927-9. arXiv:2204.04710 – reference: GuQZhuLCaiZCaiZLiZKangZLiuYEvaluation measures of the classification performance of imbalanced data setsComputational Intelligence and Intelligent Systems2009BerlinSpringer46147110.1007/978-3-642-04962-0_53 – reference: E. Arganda, M. Epele, N.I. Mileo, R.A. Morales, Machine-learning performance on Higgs-pair production associated with dark matter at the LHC. arXiv:2401.03178 – reference: BreimanLRandom ForestsMach. Learn.20014553210.1023/A:1010933404324 – reference: ATLAS Collaboration, G. Aad et al., Observation of a new particle in the search for the standard model Higgs boson with the ATLAS detector at the LHC. Phys. Lett. B 716, 1–29 (2012). https://doi.org/10.1016/j.physletb.2012.08.020. arXiv:1207.7214 – reference: ATLAS Collaboration, G. Aad et al., Search for R-parity-violating supersymmetry in a final state containing leptons and many jets with the ATLAS experiment using s=13TeV\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sqrt{s} = 13 { TeV}$$\end{document} proton–proton collision data. Eur. Phys. J. C 81, 1023 (2021). https://doi.org/10.1140/epjc/s10052-021-09761-x. arXiv:2106.09609 – reference: G. Cowan, Discovery sensitivity for a counting experiment with background uncertainty (2012) – reference: BarmanRKBhattacherjeeBChakrabortyIChoudhuryAKhanNElectroweakino searches at the HL-LHC in the baryon number violating MSSMPhys. Rev. D20211032021PhRvD.103a5003B10.1103/PhysRevD.103.015003arXiv:2003.10920 – reference: Y. Freund, An adaptive version of the boost by majority algorithm. In Proceedings of the Twelfth Annual Conference on Computational Learning Theory, pp. 102–113 (1999) – reference: H. Baer, V. Barger, H. Serce, Anomalous muon magnetic moment, supersymmetry, naturalness, LHC search limits and the landscape. Phys. Lett. B 820, 136480 (2021). https://doi.org/10.1016/j.physletb.2021.136480. arXiv:2104.07597 – reference: CMS Collaboration, A.M. Sirunyan et al., Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV. JINST 13, P05011 (2018). https://doi.org/10.1088/1748-0221/13/05/P05011. arXiv:1712.07158 – reference: DavidsonSLosadaMNeutrino masses in the R(p) violating MSSMJHEP2000050212000JHEP...05..021D10.1088/1126-6708/2000/05/021arXiv:hep-ph/0005080 – reference: HammadANojiriMMStreamlined jet tagging network assisted by jet prong structureJHEP20240617610.1007/JHEP06(2024)176arXiv:2404.14677 – reference: TrifinopoulosSB-physics anomalies: the bridge between R -parity violating supersymmetry and flavored dark matterPhys. Rev. D20191002019PhRvD.100k5022T10.1103/PhysRevD.100.115022arXiv:1904.12940 – reference: FuksBKlasenMLampreaDRRotheringMPrecision predictions for electroweak superpartner production at hadron colliders with ResumminoEur. Phys. J. C20137324802013EPJC...73.2480F10.1140/epjc/s10052-013-2480-0arXiv:1304.0790 – reference: CowanGCranmerKGrossEVitellsOAsymptotic formulae for likelihood-based tests of new physicsEur. Phys. J. C20117115542011EPJC...71.1554C10.1140/epjc/s10052-011-1554-0arXiv:1007.1727 – reference: CMS Collaboration, A.M. Sirunyan et al., Search for top squarks in final states with two top quarks and several light-flavor jets in proton-proton collisions at s\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sqrt{s}$$\end{document} = 13 TeV. Phys. Rev. D 104, 032006 (2021). https://doi.org/10.1103/PhysRevD.104.032006. arXiv:2102.06976 – reference: HEP ML Community, A living review of machine learning for particle physics – reference: LikhomanenkoTIltenPKhairullinERogozhnikovAUstyuzhaninAWilliamsMLHCb topological trigger reoptimizationJ. Phys. Conf. Ser.201566410.1088/1742-6596/664/8/082025arXiv:1510.00572 – reference: M. Chakraborti, U. Chattopadhyay, S. Poddar, How light a Higgsino or a wino dark matter can become in a compressed scenario of MSSM. JHEP 09, 064 (2017). https://doi.org/10.1007/JHEP09(2017)064. arXiv:1702.03954 – reference: BentéjacCCsörgőAMartínez-MuñozGA comparative analysis of gradient boosting algorithmsArtif. Intell. Rev.2021541937196710.1007/s10462-020-09896-5 – reference: FreundYSchapireREA decision-theoretic generalization of on-line learning and an application to boostingJ. Comput. Syst. Sci.199755119139147305510.1006/jcss.1997.1504 – reference: ATLAS Collaboration, G. Aad et al., Search for new phenomena with top quark pairs in final states with one lepton, jets, and missing transverse momentum in pp\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$pp$$\end{document} collisions at s\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sqrt{s}$$\end{document} = 13 TeV with the ATLAS detector. JHEP 04, 174 (2021). https://doi.org/10.1007/JHEP04(2021)174. arXiv:2012.03799 – reference: S.P. Martin, A Supersymmetry primer. Adv. Ser. Direct. High Energy Phys. 18, 1–98 (1998). https://doi.org/10.1142/9789812839657_0001. arXiv:hep-ph/9709356 – reference: CMS Collaboration, A.M. Sirunyan et al., Search for charged Higgs bosons in the H±\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text{ H}^{\pm }$$\end{document}→\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\rightarrow$$\end{document}τ±ντ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau ^{\pm }\nu _\tau$$\end{document} decay channel in proton-proton collisions at s=\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sqrt{s} =$$\end{document} 13 TeV. JHEP 07, 142 (2019). https://doi.org/10.1007/JHEP07(2019)142. arXiv:1903.04560 – reference: NiazkarMMenapaceABrentanBPiraeiRJimenezDDhawanPApplications of xgboost in water resources engineering: a systematic literature review (Dec 2018-May 2023)Environ. Model. Softw.20241740210.1016/j.envsoft.2024.105971 – reference: CMS Collaboration, A.M. Sirunyan et al., Search for R\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R$$\end{document}-parity violating supersymmetry in pp collisions at s=\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sqrt{s} =$$\end{document} 13 TeV using b jets in a final state with a single lepton, many jets, and high sum of large-radius jet masses. Phys. Lett. B 783, 114–139 (2018). https://doi.org/10.1016/j.physletb.2018.06.028. arXiv:1712.08920 – reference: T. Chen, C. Guestrin, XGBoost: a scalable tree boosting system. arXiv:1603.02754 – reference: S. Lundberg, S.-I. Lee, A unified approach to interpreting model predictions (2017) – reference: CollaborationCDFAaltonenTObservation of single top quark production and measurement of |Vtb\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$V_{tb}$$\end{document}| with CDFPhys. Rev. D2010822010PhRvD..82k2005A10.1103/PhysRevD.82.112005arXiv:1004.1181 – reference: KomiskePTMetodievEMSchwartzMDDeep learning in color: towards automated quark/gluon jet discriminationJHEP2017011102017JHEP...01..110K10.1007/JHEP01(2017)110arXiv:1612.01551 – reference: AllanachBCKomCHLepton number violating mSUGRA and neutrino massesJHEP2008040812008JHEP...04..081A10.1088/1126-6708/2008/04/081arXiv:0712.0852 – reference: Cms phase 1 heavy flavour identification performance and developments, cms detector performance note, cms-dp-2017-013 (2017) – reference: J. Shlomi, P. Battaglia, J.-R. Vlimant, Graph neural networks in particle physics. arXiv:2007.13681 – reference: BarmanRKBhattacherjeeBChoudhuryAChowdhuryDLahiriJRaySCurrent status of MSSM Higgs sector with LHC 13 TeV dataEur. Phys. J. Plus201913415010.1140/epjp/i2019-12566-5arXiv:1608.02573 – reference: R. Di Sipio, M. Faucci Giannelli, S. Ketabchi Haghighat, S. Palazzo, DijetGAN: a generative-adversarial network approach for the simulation of QCD Dijet events at the LHC. JHEP 08, 110 (2019). https://doi.org/10.1007/JHEP08(2019)110. arXiv:1903.02433 – reference: H.K. Dreiner, Y.S. Koay, D. Köhler, V.M. Lozano, J. Montejo Berlingen, S. Nangia et al., The ABC of RPV: classification of R-parity violating signatures at the LHC for small couplings. JHEP 07, 215 (2023). https://doi.org/10.1007/JHEP07(2023)215. arXiv:2306.07317 – reference: DattaAGangulyNThe past, present and future of the heavier electroweakinos in the light of LHC and other dataJHEP2019011032019JHEP...01..103D10.1007/JHEP01(2019)103arXiv:1809.05129 – reference: A. Choudhury, A. Mondal, S. Mondal, S. Sarkar, Slepton searches in the trilinear RPV SUSY scenarios at the HL-LHC and HE-LHC. arXiv:2310.07532 – reference: CerianiLVermePThe origins of the gini index: extracts from variabilit e mutabilit, by corrado giniJ. Econ. Inequal.1912102012421443 – reference: BagnaschiELikelihood analysis of the pMSSM11 in light of LHC 13-TeV dataEur. Phys. J. C2018782562018EPJC...78..256B10.1140/epjc/s10052-018-5697-0arXiv:1710.11091 – reference: C. Bishop, Pattern recognition and machine learning 16, 140–155. 01 (2006). https://doi.org/10.1117/1.2819119 – reference: BaldiPBauerKEngCSadowskiPWhitesonDJet substructure classification in high-energy physics with deep neural networksPhys. Rev. D2016932016PhRvD..93i4034B10.1103/PhysRevD.93.094034arXiv:1603.09349 – reference: ATLAS Collaboration, G. Aad et al., Search for squarks and gluinos in final states with jets and missing transverse momentum using 139 fb-1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text{ fb}^{-1}$$\end{document} of s\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sqrt{s}$$\end{document} =13 TeV pp\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$pp$$\end{document} collision data with the ATLAS detector. JHEP 02, 143 (2021). https://doi.org/10.1007/JHEP02(2021)143. arXiv:2010.14293 – reference: J. Cao, L. Meng, Y. Yue, Electron and muon anomalous magnetic moments in the Z3-NMSSM. Phys. Rev. D 108, 035043 (2023). https://doi.org/10.1103/PhysRevD.108.035043. arXiv:2306.06854 – reference: K. Kowalska, L. Roszkowski, E.M. Sessolo, A.J. Williams, GUT-inspired SUSY and the muon g -\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-$$\end{document} 2 anomaly: prospects for LHC 14 TeV. JHEP 06, 020 (2015). https://doi.org/10.1007/JHEP06(2015)020. arXiv:1503.08219 – reference: R.K. Barman, G. Bélanger, B. Bhattacherjee, R. Godbole, R. Sengupta, Current status of the light neutralino thermal dark matter in the phenomenological MSSM. arXiv:2402.07991 – reference: RenJWuLYangJMZhaoJExploring supersymmetry with machine learningNucl. Phys. B201994310.1016/j.nuclphysb.2019.114613arXiv:1708.06615 – reference: KeGMengQFinleyTWangTChenWMaWLightgbm: a highly efficient gradient boosting decision treeAdv. Neural Inf. Process. Syst.20173031463154 – reference: ChakrabortyAChakrabortySProbing (g-2)μ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(g-2)_{\mu }$$\end{document} at the LHC in the paradigm of R\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R$$\end{document}-parity violating MSSMPhys. Rev. D2016932016PhRvD..93g5035C10.1103/PhysRevD.93.075035arXiv:1511.08874 – reference: T. Sjostrand, S. Mrenna, P.Z. Skands, PYTHIA 6.4 physics and manual. JHEP 05, 026 (2006). https://doi.org/10.1088/1126-6708/2006/05/026. arXiv:hep-ph/0603175 – reference: B.P. Roe, H.-J. Yang, J. Zhu, Y. Liu, I. Stancu, G. McGregor, Boosted decision trees as an alternative to artificial neural networks for particle identification. Nucl. Instrum. Methods Phys. Res. Sect. A Accel. Spectrom. Detect. Assoc. Equip. 543, 577–584 (2005). https://doi.org/10.1016/j.nima.2004.12.018 – reference: MusellaPPandolfiFFast and accurate simulation of particle detectors using generative adversarial networksComput. Softw. Big Sci.20182810.1007/s41781-018-0015-yarXiv:1805.00850 – reference: A.L. Samuel, Some studies in machine learning using the game of checkers. I (Springer, New York, 1988), pp.335–365. https://doi.org/10.1007/978-1-4613-8716-9_14 – reference: GrossmanYRakshitSNeutrino masses in R-parity violating supersymmetric modelsPhys. Rev. D2004692004PhRvD..69i3002G10.1103/PhysRevD.69.093002arXiv:hep-ph/0311310 – reference: AlwallJFrederixRFrixioneSHirschiVMaltoniFMattelaerOThe automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulationsJHEP2014070792014JHEP...07..079A10.1007/JHEP07(2014)079arXiv:1405.0301 – reference: CollaborationCMSAcostaDBoosted decision trees in the level-1 Muon endcap trigger at CMSJ. Phys. Conf. Ser.2018108510.1088/1742-6596/1085/4/042042 – reference: ATLAS Collaboration, G. Aad et al., Search for chargino–neutralino pair production in final states with three leptons and missing transverse momentum in s=13\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sqrt{s} = 13$$\end{document} TeV pp collisions with the ATLAS detector. Eur. Phys. J. C 81, 1118 (2021). https://doi.org/10.1140/epjc/s10052-021-09749-7. arXiv:2106.01676 – reference: M. Chakraborti, L. Roszkowski, S. Trojanowski, GUT-constrained supersymmetry and dark matter in light of the new (g-2)μ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(g-2)_\mu$$\end{document} determination. JHEP 05, 252 (2021). https://doi.org/10.1007/JHEP05(2021)252. arXiv:2104.04458 – reference: AbdughaniMRenJWuLYangJMZhaoJSupervised deep learning in high energy phenomenology: a mini reviewCommun. Theor. Phys.2019719552019CoTPh..71..955A401261810.1088/0253-6102/71/8/955arXiv:1905.06047 – reference: KronheimBSKucheraMPProsperHBKarboABayesian neural networks for fast SUSY predictionsPhys. Lett. B202181310.1016/j.physletb.2020.136041arXiv:2007.04506 – reference: M.I. Ali, M. Chakraborti, U. Chattopadhyay, S. Mukherjee, Muon and electron (g-2)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(g-2)$$\end{document} anomalies with non-holomorphic interactions in MSSM. Eur. Phys. J. C 83, 60 (2023). https://doi.org/10.1140/epjc/s10052-023-11216-4. arXiv:2112.09867 – reference: BhatPCMultivariate analysis methods in particle physicsAnn. Rev. Nucl. Part. Sci.2011612813092011ARNPS..61..281B10.1146/annurev.nucl.012809.104427 – reference: M. Chakraborti, S. Heinemeyer, I. Saha, The new ‘MUON G-2’ result and supersymmetry. Eur. Phys. J. C 81, 1114 (2021). https://doi.org/10.1140/epjc/s10052-021-09900-4. arXiv:2104.03287 – reference: B. Bhattacherjee, A. Choudhury, K. Ghosh, S. Poddar, Compressed supersymmetry at 14 TeV LHC. Phys. Rev. D 89, 037702 (2014). https://doi.org/10.1103/PhysRevD.89.037702. arXiv:1308.1526 – reference: A. Choudhury, A. Datta, Many faces of low mass neutralino dark matter in the unconstrained MSSM. LHC data and new signals. JHEP 06, 006 (2012). https://doi.org/10.1007/JHEP06(2012)006. arXiv:1203.4106 – reference: A. Choudhury, S. Mondal, Revisiting the exclusion limits from direct Chargino-Neutralino production at the LHC. Phys. Rev. D 94, 055024 (2016). https://doi.org/10.1103/PhysRevD.94.055024. arXiv:1603.05502 – reference: ATLAS Collaboration, G. Aad et al., Search for supersymmetry in events with four or more charged leptons in 139 fb-1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text{ fb}^{-1}$$\end{document} of s\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sqrt{s}$$\end{document} = 13 TeV pp collisions with the ATLAS detector, JHEP 07, 167 (2021). https://doi.org/10.1007/JHEP07(2021)167. arXiv:2103.11684 – reference: ATLAS Collaboration, G. Aad et al., ATLAS b-jet identification performance and efficiency measurement with tt¯\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$t{\bar{t}}$$\end{document} events in pp collisions at s=13\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sqrt{s}=13$$\end{document} TeV. Eur. Phys. J. C 79, 970 (2019). https://doi.org/10.1140/epjc/s10052-019-7450-8. arXiv:1907.05120 – reference: A. Choudhury, S. Rao, L. Roszkowski, Impact of LHC data on muon g-2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g-2$$\end{document} solutions in a vectorlike extension of the constrained MSSM. Phys. Rev. D 96, 075046 (2017). https://doi.org/10.1103/PhysRevD.96.075046. arXiv:1708.05675 – reference: SaitoTRehmsmeierMThe precision-recall plot is more informative than the roc plot when evaluating binary classifiers on imbalanced datasetsPLoS One201510.1371/journal.pone.0118432 – reference: BridgesMCranmerKFerozFHobsonMRuiz de AustriRTrottaRA coverage study of the CMSSM based on ATLAS sensitivity using fast neural networks techniquesJHEP2011030122011JHEP...03..012B10.1007/JHEP03(2011)012arXiv:1011.4306 – reference: H. Banerjee, P. Byakti, S. Roy, Supersymmetric gauged U(1)Lμ-Lτ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text{ U }(1)_{L_{\mu }-L_{\tau }}$$\end{document} model for neutrinos and the muon (g-2)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(g-2)$$\end{document} anomaly. Phys. Rev. D 98, 075022 (2018). https://doi.org/10.1103/PhysRevD.98.075022. arXiv:1805.04415 – reference: CollaborationCMSChatrchyanSObservation of a new boson at a mass of 125 GeV with the CMS experiment at the LHCPhys. Lett. B201271630612012PhLB..716...30C10.1016/j.physletb.2012.08.021arXiv:1207.7235 – reference: FerozFHobsonMPMultimodal nested sampling: an efficient and robust alternative to MCMC methods for astronomical data analysisMon. Not. R. Astron. Soc.20083844492008MNRAS.384..449F10.1111/j.1365-2966.2007.12353.xarXiv:0704.3704 – reference: J. Chakrabortty, A. Choudhury, S. Mondal, Non-universal Gaugino mass models under the lamppost of muon (g-2). JHEP 07, 038 (2015). https://doi.org/10.1007/JHEP07(2015)038. arXiv:1503.08703 – reference: A. Luque, A. Carrasco, A. MartÃn, A. de las Heras, The impact of class imbalance in classification performance metrics based on the binary confusion matrix. Pattern Recognit. 91, 216–231 (2019). https://doi.org/10.1016/j.patcog.2019.02.023 – reference: BarbierRR-parity violating supersymmetryPhys. Rep.200542012022005PhR...420....1B10.1016/j.physrep.2005.08.006arXiv:hep-ph/0406039 – reference: CoganJKaganMStraussESchwarztmanAJet-images: computer vision inspired techniques for jet taggingJHEP2015021182015JHEP...02..118C10.1007/JHEP02(2015)118arXiv:1407.5675 – reference: A. Butter, T. Plehn, R. Winterhalder, How to GAN LHC events. SciPost Phys. 7, 075 (2019). https://doi.org/10.21468/SciPostPhys.7.6.075. arXiv:1907.03764 – reference: T. Bylander, L. Tate, Using validation sets to avoid overfitting in adaboost. In FLAIRS, pp. 544–549 (2006) – reference: WeissGMProvostFLearning when training data are costly: the effect of class distribution on tree inductionJ. Artif. Intell. Res.20031931535410.1613/jair.1199 – reference: TMVA Collaboration, A. Hocker et al., TMVA—toolkit for multivariate data analysis. arXiv:physics/0703039 – reference: HastieTTibshiraniRFriedmanJHFriedmanJHThe Elements of Statistical Learning: Data Mining, Inference, and Prediction2009BerlinSpringer10.1007/978-0-387-84858-7 – reference: LinJBhimjiWNachmanBMachine learning templates for QCD factorization in the search for physics beyond the standard modelJHEP2019051812019JHEP...05..181L397340610.1007/JHEP05(2019)181arXiv:1903.02556 – reference: J. Dutta, P. Konar, S. Mondal, B. Mukhopadhyaya, S.K. Rai, A revisit to a compressed supersymmetric spectrum with 125 GeV Higgs. JHEP 01, 051 (2016). https://doi.org/10.1007/JHEP01(2016)051. arXiv:1511.09284 – reference: RoySMukhopadhyayaBSome implications of a supersymmetric model with R-parity breaking bilinear interactionsPhys. Rev. D199755702070291997PhRvD..55.7020R10.1103/PhysRevD.55.7020arXiv:hep-ph/9612447 – reference: YangH-JRoeBPZhuJStudies of boosted decision trees for MiniBooNE particle identificationNucl. Instrum. Methods A20055553703852005NIMPA.555..370Y10.1016/j.nima.2005.09.022arXiv:physics/0508045 – reference: GAMBIT Collaboration, P. Athron et al., A global fit of the MSSM with GAMBIT. Eur. Phys. J. C 77, 879 (2017). https://doi.org/10.1140/epjc/s10052-017-5196-8. arXiv:1705.07917 – reference: CMS Collaboration, A.M. Sirunyan et al., Search for resonant production of second-generation sleptons with same-sign dimuon events in proton-proton collisions at s=\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sqrt{s} =$$\end{document} 13 TeV. Eur. Phys. J. C 79, 305 (2019). https://doi.org/10.1140/epjc/s10052-019-6800-x. arXiv:1811.09760 – reference: ChakrabortyALimSHNojiriMMInterpretable deep learning for two-prong jet classification with jet spectraJHEP2019071352019JHEP...07..135C10.1007/JHEP07(2019)135arXiv:1904.02092 – reference: FriedmanJHastieTTibshiraniRAdditive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors)Ann. Stat.20002833740710.1214/aos/1016218223 – reference: M. Endo, K. Hamaguchi, S. Iwamoto, T. Kitahara, Supersymmetric interpretation of the muon g - 2 anomaly. JHEP 07, 075 (2021). https://doi.org/10.1007/JHEP07(2021)075. arXiv:2104.03217 – reference: M.D. Schwartz, Modern machine learning and particle physics. arXiv:2103.12226 – reference: DELPHES 3 Collaboration, J. de Favereau, C. Delaere, P. Demin, A. Giammanco, V. Lemaître, A. Mertens et al., DELPHES 3, a modular framework for fast simulation of a generic collider experiment. JHEP 02, 057 (2014). https://doi.org/10.1007/JHEP02(2014)057. arXiv:1307.6346 – reference: A. Choudhury, A. Mondal, S. Mondal, Status of R-parity violating SUSY. arXiv:2402.04040 – reference: BhattacherjeeBChakrabortyAChoudhuryAStatus of the MSSM Higgs sector using global analysis and direct search bounds, and future prospects at the high luminosity LHCPhys. Rev. D2015922015PhRvD..92i3007B10.1103/PhysRevD.92.093007arXiv:1504.04308 – reference: H.-J. Yang, B.P. Roe, J. Zhu, Studies of boosted decision trees for miniboone particle identification. Nucl. Instrum. Methods Phys. Res. Sect. A Accel. Spectrom. Detect. Assoc. Equip. 555, 370–385 (2005). https://doi.org/10.1016/j.nima.2005.09.022 – reference: R.K. Barman, G. Bélanger, B. Bhattacherjee, R.M. Godbole, R. Sengupta, Is light neutralino thermal dark matter in the phenomenological minimal supersymmetric standard model ruled out? Phys. Rev. Lett. 131, 011802 (2023). https://doi.org/10.1103/PhysRevLett.131.011802. arXiv:2207.06238 – reference: Preface to special issue on “learning to discover”. Int. J. Mod. Phys. A 35, 2002003 (2020). https://doi.org/10.1142/S0217751X20020030 – reference: BuckleyAShiltonAWhiteMJFast supersymmetry phenomenology at the Large Hadron Collider using machine learning techniquesComput. Phys. Commun.20121839609702012CoPhC.183..960B10.1016/j.cpc.2011.12.026arXiv:1106.4613 – reference: M. Hossin, S.M.N, A review on evaluation metrics for data classification evaluations. Int. J. Data Min. Knowl. Manag. Process 5, 01–11 (2015). https://doi.org/10.5121/ijdkp.2015.5201 – reference: DasDHatiCKumarGMahajanNScrutinizing R\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R$$\end{document}-parity violating interactions in light of RK(*)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R_{K^{(\ast )}}$$\end{document} dataPhys. Rev. D2017962017PhRvD..96i5033D10.1103/PhysRevD.96.095033arXiv:1705.09188 – reference: BaerHTataXWeak Scale Supersymmetry: From Superfields to Scattering Events2006CambridgeCambridge University Press10.1017/CBO9780511617270 – reference: BolsEKieselerJVerzettiMStoyeMStakiaAJet flavour classification using DeepJetJINST202015P120122020JInst..1512012B10.1088/1748-0221/15/12/P12012arXiv:2008.10519 – reference: WangRAdaboost for feature selection, classification and its relation with svm, a reviewPhys. Procedia2012258008072012PhPro..25..800W10.1016/j.phpro.2012.03.160 – reference: D0 collaboration, V.M. Abazov et al., Evidence for production of single top quarks and first direct measurement of |Vtb|. Phys. Rev. Lett. 98, 181802 (2007). https://doi.org/10.1103/PhysRevLett.98.181802. arXiv:hep-ex/0612052 – reference: A.V. Dorogush, V. Ershov, A. Gulin, Catboost: gradient boosting with categorical features support. arXiv:1810.11363 (2018) – reference: ATLAS Collaboration, G. Aad et al., A search for R-parity-violating supersymmetry in final states containing many jets in pp\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$pp$$\end{document} collisions at s=13TeV\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sqrt{s} = 13\,\text{ TeV }$$\end{document} with the ATLAS detector. arXiv:2401.16333 – reference: HammadAMorettiSNojiriMMulti-scale cross-attention transformer encoder for event classificationJHEP2024031442024JHEP...03..144H10.1007/JHEP03(2024)144arXiv:2401.00452 – reference: BreimanLFriedmanJOlshenRAStoneCJClassification and Regression Trees1984Boca RatonChapman and Hall – reference: AltmannshoferWDevPSBSoniASuiYAddressing RD(∗)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text{ R}_{D^{(*)}}$$\end{document}, RK(∗)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text{ R}_{K^{(*)}}$$\end{document}, muon g-2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g-2$$\end{document} and ANITA anomalies in a minimal R\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R$$\end{document}-parity violating supersymmetric frameworkPhys. Rev. D20201022020PhRvD.102a5031A10.1103/PhysRevD.102.015031arXiv:2002.12910 – reference: GAMBIT Collaboration, P. Athron et al., Global fits of GUT-scale SUSY models with GAMBIT. Eur. Phys. J. C 77, 824 (2017). https://doi.org/10.1140/epjc/s10052-017-5167-0. arXiv:1705.07935 – reference: ATLAS Collaboration, Quark versus gluon jet tagging using jet images with the ATLAS detector – reference: DíazMARiveraMRojasNOn neutrino masses in the MSSM with BRpVNucl. Phys. B20148873383572014NuPhB.887..338D10.1016/j.nuclphysb.2014.08.012arXiv:1401.7357 – reference: D0 Collaboration, V.M. Abazov et al., Evidence for production of single top quarks. Phys. Rev. D 78, 012005 (2008). https://doi.org/10.1103/PhysRevD.78.012005. arXiv:0803.0739 – reference: J.G. Carbonell, R.S. Michalski, T.M. Mitchell, An overview of machine learning. Mach. Learn. 3–23 (1983) – reference: ChoudhuryAMitraSMondalAMondalSBilinear R-parity violating supersymmetry under the light of neutrino oscillation. Higgs and flavor dataJHEP2024020042024JHEP...02..004C10.1007/JHEP02(2024)004arXiv:2305.15211 – reference: Search for supersymmetry at the high luminosity LHC with the ATLAS experiment, ATL-PHYS-PUB-2014-010 (2014) – reference: QuinlanJSimplifying decision treesInt. J. Man Mach. Stud.19872722123410.1016/S0020-7373(87)80053-6 – reference: ATLAS Collaboration, G. Aad et al., Search for electroweak production of supersymmetric particles in final states with two τ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau$$\end{document}-leptons in s\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sqrt{s}$$\end{document} = 13 TeV pp\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$pp$$\end{document} collisions with the ATLAS detector. arXiv:2402.00603 – reference: L. Breiman, J. Friedman, R. Olshen, C. Stone, Classification and regression trees. 10, 2017, https://doi.org/10.1201/9781315139470 – reference: D. Chowdhury, K.M. Patel, X. Tata, S.K. Vempati, Indirect searches of the degenerate MSSM. Phys. Rev. D 95, 075025 (2017). https://doi.org/10.1103/PhysRevD.95.075025. arXiv:1612.06471 – reference: Y. He, L. Meng, Y. Yue, D. Zhang, Impact of the recent measurement of (g-2)μ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mu }$$\end{document}, the LHC search for supersymmetry, and the LZ experiment on the minimal supersymmetric standard model. Phys. Rev. D 108, 115010 (2023). https://doi.org/10.1103/PhysRevD.108.115010. arXiv:2303.02360 – reference: ChoudhuryADattaANeutralino dark matter confronted by the LHC constraints on Electroweak SUSY signalsJHEP2013091192013JHEP...09..119C10.1007/JHEP09(2013)119arXiv:1305.0928 – ident: 1308_CR14 doi: 10.1007/JHEP11(2015)050 – volume: 06 start-page: 176 year: 2024 ident: 1308_CR52 publication-title: JHEP doi: 10.1007/JHEP06(2024)176 – ident: 1308_CR19 doi: 10.1007/JHEP08(2022)124 – volume: 30 start-page: 3146 year: 2017 ident: 1308_CR177 publication-title: Adv. Neural Inf. Process. Syst. – ident: 1308_CR187 – volume: 28 start-page: 337 year: 2000 ident: 1308_CR168 publication-title: Ann. Stat. doi: 10.1214/aos/1016218223 – ident: 1308_CR110 doi: 10.1140/epjc/s10052-019-7450-8 – volume: 83 start-page: 379 year: 2023 ident: 1308_CR129 publication-title: Eur. Phys. J. C doi: 10.1140/epjc/s10052-023-11532-9 – ident: 1308_CR164 – ident: 1308_CR10 doi: 10.1007/JHEP09(2017)026 – volume: 02 start-page: 160 year: 2021 ident: 1308_CR91 publication-title: JHEP doi: 10.1007/JHEP02(2021)160 – volume: 12 start-page: 148 year: 2023 ident: 1308_CR161 publication-title: JHEP doi: 10.1007/JHEP12(2023)148 – volume: 134 start-page: 150 year: 2019 ident: 1308_CR136 publication-title: Eur. Phys. J. Plus doi: 10.1140/epjp/i2019-12566-5 – volume: 61 start-page: 281 year: 2011 ident: 1308_CR44 publication-title: Ann. Rev. Nucl. Part. Sci. doi: 10.1146/annurev.nucl.012809.104427 – ident: 1308_CR137 doi: 10.1007/JHEP07(2020)126 – ident: 1308_CR118 doi: 10.1007/JHEP02(2021)143 – ident: 1308_CR170 – volume: 887 start-page: 338 year: 2014 ident: 1308_CR145 publication-title: Nucl. Phys. B doi: 10.1016/j.nuclphysb.2014.08.012 – volume: 12 start-page: 024 year: 2008 ident: 1308_CR82 publication-title: JHEP doi: 10.1088/1126-6708/2008/12/024 – ident: 1308_CR115 doi: 10.1103/PhysRevD.97.072016 – ident: 1308_CR1 doi: 10.1142/9789812839657_0001 – volume: 19 start-page: 315 year: 2003 ident: 1308_CR62 publication-title: J. Artif. Intell. Res. doi: 10.1613/jair.1199 – ident: 1308_CR8 doi: 10.1007/JHEP09(2017)064 – ident: 1308_CR58 doi: 10.1016/B978-0-08-051054-5.50005-4 – ident: 1308_CR18 doi: 10.1016/j.physletb.2021.136480 – volume: 384 start-page: 449 year: 2008 ident: 1308_CR80 publication-title: Mon. Not. R. Astron. Soc. doi: 10.1111/j.1365-2966.2007.12353.x – ident: 1308_CR79 – volume: 174 issue: 02 year: 2024 ident: 1308_CR60 publication-title: Environ. Model. Softw. doi: 10.1016/j.envsoft.2024.105971 – volume: 71 start-page: 955 year: 2019 ident: 1308_CR50 publication-title: Commun. Theor. Phys. doi: 10.1088/0253-6102/71/8/955 – ident: 1308_CR33 doi: 10.1007/s11433-022-1927-9 – ident: 1308_CR84 doi: 10.1140/epjc/s10052-017-5167-0 – volume: 27 start-page: 221 year: 1987 ident: 1308_CR166 publication-title: Int. J. Man Mach. Stud. doi: 10.1016/S0020-7373(87)80053-6 – volume: 70 year: 2004 ident: 1308_CR121 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.70.015007 – volume: 78 start-page: 256 year: 2018 ident: 1308_CR134 publication-title: Eur. Phys. J. C doi: 10.1140/epjc/s10052-018-5697-0 – volume: 398 start-page: 1601 year: 2009 ident: 1308_CR81 publication-title: Mon. Not. R. Astron. Soc. doi: 10.1111/j.1365-2966.2009.14548.x – ident: 1308_CR152 doi: 10.1007/JHEP07(2021)167 – ident: 1308_CR4 – volume: 04 start-page: 081 year: 2008 ident: 1308_CR144 publication-title: JHEP doi: 10.1088/1126-6708/2008/04/081 – ident: 1308_CR59 – volume: 55 start-page: 119 year: 1997 ident: 1308_CR174 publication-title: J. Comput. Syst. Sci. doi: 10.1006/jcss.1997.1504 – volume: 79 start-page: 944 year: 2019 ident: 1308_CR97 publication-title: Eur. Phys. J. C doi: 10.1140/epjc/s10052-019-7437-5 – volume: 93 year: 2016 ident: 1308_CR102 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.93.094034 – volume: 555 start-page: 370 year: 2005 ident: 1308_CR169 publication-title: Nucl. Instrum. Methods A doi: 10.1016/j.nima.2005.09.022 – volume-title: Weak Scale Supersymmetry: From Superfields to Scattering Events year: 2006 ident: 1308_CR3 doi: 10.1017/CBO9780511617270 – ident: 1308_CR32 doi: 10.1103/PhysRevD.94.055024 – volume: 07 start-page: 069 year: 2016 ident: 1308_CR101 publication-title: JHEP doi: 10.1007/JHEP07(2016)069 – ident: 1308_CR183 doi: 10.1088/1126-6708/2006/05/026 – ident: 1308_CR66 doi: 10.1016/j.patcog.2019.02.023 – volume: 92 year: 2015 ident: 1308_CR135 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.92.093007 – volume: 34 start-page: 1930019 year: 2020 ident: 1308_CR46 publication-title: Int. J. Mod. Phys. A doi: 10.1142/S0217751X19300199 – ident: 1308_CR153 doi: 10.1140/epjc/s10052-021-09761-x – ident: 1308_CR35 doi: 10.1007/978-1-4613-8716-9_14 – volume: 45 start-page: 5 year: 2001 ident: 1308_CR171 publication-title: Mach. Learn. doi: 10.1023/A:1010933404324 – ident: 1308_CR108 – ident: 1308_CR38 doi: 10.1103/PhysRevLett.98.181802 – ident: 1308_CR40 – ident: 1308_CR56 doi: 10.1117/1.2819119 – ident: 1308_CR132 doi: 10.1007/JHEP09(2018)139 – volume: 8 start-page: P02013 year: 2013 ident: 1308_CR76 publication-title: JINST doi: 10.1088/1748-0221/8/02/P02013 – volume: 07 start-page: 135 year: 2019 ident: 1308_CR104 publication-title: JHEP doi: 10.1007/JHEP07(2019)135 – volume: 716 start-page: 30 year: 2012 ident: 1308_CR114 publication-title: Phys. Lett. B doi: 10.1016/j.physletb.2012.08.021 – ident: 1308_CR5 – ident: 1308_CR6 doi: 10.1103/PhysRevD.89.037702 – volume: 14 start-page: 1612 year: 1999 ident: 1308_CR172 publication-title: J. Jpn. Soc. Artif. Intell. – ident: 1308_CR99 doi: 10.1140/epjc/s10052-019-6847-8 – ident: 1308_CR130 – volume: 03 start-page: 144 year: 2024 ident: 1308_CR51 publication-title: JHEP doi: 10.1007/JHEP03(2024)144 – start-page: 461 volume-title: Computational Intelligence and Intelligent Systems year: 2009 ident: 1308_CR67 doi: 10.1007/978-3-642-04962-0_53 – volume: 01 start-page: 103 year: 2019 ident: 1308_CR180 publication-title: JHEP doi: 10.1007/JHEP01(2019)103 – volume: 77 start-page: 257 year: 2017 ident: 1308_CR87 publication-title: Eur. Phys. J. C doi: 10.1140/epjc/s10052-017-4814-9 – volume: 943 year: 2019 ident: 1308_CR96 publication-title: Nucl. Phys. B doi: 10.1016/j.nuclphysb.2019.114613 – volume: 71 start-page: 1554 year: 2011 ident: 1308_CR69 publication-title: Eur. Phys. J. C doi: 10.1140/epjc/s10052-011-1554-0 – ident: 1308_CR106 doi: 10.1088/1748-0221/13/05/P05011 – ident: 1308_CR68 – ident: 1308_CR27 doi: 10.1140/epjc/s10052-021-09900-4 – ident: 1308_CR105 – volume: 37 start-page: 2250197 year: 2022 ident: 1308_CR123 publication-title: Int. J. Mod. Phys. A doi: 10.1142/S0217751X22501974 – ident: 1308_CR42 doi: 10.1142/S0217751X20020030 – volume: 1 start-page: 2 year: 2017 ident: 1308_CR139 publication-title: Comput. Softw. Big Sci. doi: 10.1007/s41781-017-0002-8 – ident: 1308_CR31 doi: 10.1007/JHEP07(2015)038 – volume: 10 start-page: 421 issue: 2012 year: 1912 ident: 1308_CR165 publication-title: J. Econ. Inequal. – volume: 1085 year: 2018 ident: 1308_CR74 publication-title: J. Phys. Conf. Ser. doi: 10.1088/1742-6596/1085/4/042042 – ident: 1308_CR43 – ident: 1308_CR186 doi: 10.1007/JHEP02(2014)057 – ident: 1308_CR156 doi: 10.1103/PhysRevD.104.032006 – volume: 01 start-page: 110 year: 2017 ident: 1308_CR103 publication-title: JHEP doi: 10.1007/JHEP01(2017)110 – ident: 1308_CR167 doi: 10.1145/307400.307419 – volume: 2 start-page: 8 year: 2018 ident: 1308_CR95 publication-title: Comput. Softw. Big Sci. doi: 10.1007/s41781-018-0015-y – volume-title: Classification and Regression Trees year: 1984 ident: 1308_CR162 – ident: 1308_CR158 doi: 10.1007/JHEP07(2023)215 – volume: 05 start-page: 021 year: 2000 ident: 1308_CR142 publication-title: JHEP doi: 10.1088/1126-6708/2000/05/021 – volume: 04 start-page: 015 year: 2022 ident: 1308_CR54 publication-title: JHEP doi: 10.1007/JHEP04(2022)015 – ident: 1308_CR160 – ident: 1308_CR75 – volume: 60 start-page: 84 year: 2012 ident: 1308_CR111 publication-title: Commun. ACM doi: 10.1145/3065386 – ident: 1308_CR131 doi: 10.1007/JHEP11(2018)085 – ident: 1308_CR37 doi: 10.1103/PhysRevD.78.012005 – ident: 1308_CR29 doi: 10.1140/epjc/s10052-023-11216-4 – volume: 07 start-page: 079 year: 2014 ident: 1308_CR184 publication-title: JHEP doi: 10.1007/JHEP07(2014)079 – ident: 1308_CR22 doi: 10.1007/JHEP05(2021)252 – volume: 55 start-page: 7020 year: 1997 ident: 1308_CR143 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.55.7020 – volume: 420 start-page: 1 year: 2005 ident: 1308_CR116 publication-title: Phys. Rep. doi: 10.1016/j.physrep.2005.08.006 – volume: 105 year: 2022 ident: 1308_CR140 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.105.095039 – ident: 1308_CR163 doi: 10.1201/9781315139470 – volume: 82 year: 2010 ident: 1308_CR36 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.82.112005 – volume: 664 year: 2015 ident: 1308_CR77 publication-title: J. Phys. Conf. Ser. doi: 10.1088/1742-6596/664/8/082025 – volume: 05 start-page: 181 year: 2019 ident: 1308_CR94 publication-title: JHEP doi: 10.1007/JHEP05(2019)181 – volume: 813 year: 2021 ident: 1308_CR90 publication-title: Phys. Lett. B doi: 10.1016/j.physletb.2020.136041 – volume: 100 year: 2019 ident: 1308_CR149 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.100.115022 – volume: 103 year: 2021 ident: 1308_CR179 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.103.015003 – ident: 1308_CR113 doi: 10.1016/j.physletb.2012.08.020 – volume: 02 start-page: 066 year: 2019 ident: 1308_CR150 publication-title: JHEP doi: 10.1007/JHEP02(2019)066 – ident: 1308_CR92 doi: 10.1007/JHEP08(2019)110 – ident: 1308_CR98 – volume: 55 start-page: 119 year: 1997 ident: 1308_CR73 publication-title: J. Comput. Syst. Sci. doi: 10.1006/jcss.1997.1504 – ident: 1308_CR83 doi: 10.1111/j.1365-2966.2009.14548.x – ident: 1308_CR119 – ident: 1308_CR21 doi: 10.1007/JHEP07(2021)075 – ident: 1308_CR112 doi: 10.1140/epjc/s10052-020-08509-3 – volume: 96 year: 2017 ident: 1308_CR151 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.96.095033 – volume: 93 year: 2016 ident: 1308_CR146 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.93.075035 – ident: 1308_CR49 – volume: 02 start-page: 004 year: 2024 ident: 1308_CR86 publication-title: JHEP doi: 10.1007/JHEP02(2024)004 – ident: 1308_CR23 doi: 10.1103/PhysRevD.96.075046 – ident: 1308_CR55 – ident: 1308_CR13 doi: 10.1103/PhysRevD.108.115010 – ident: 1308_CR188 – volume: 183 start-page: 960 year: 2012 ident: 1308_CR89 publication-title: Comput. Phys. Commun. doi: 10.1016/j.cpc.2011.12.026 – ident: 1308_CR128 – ident: 1308_CR11 doi: 10.1140/epjst/e2020-000198-1 – ident: 1308_CR155 doi: 10.1016/j.physletb.2018.06.028 – ident: 1308_CR64 doi: 10.5121/ijdkp.2015.5201 – ident: 1308_CR30 doi: 10.1007/JHEP06(2015)020 – ident: 1308_CR41 – volume-title: Theory and Phenomenology of Sparticles: An Account of Four-Dimensional N year: 2004 ident: 1308_CR2 – ident: 1308_CR25 doi: 10.1103/PhysRevD.98.075022 – ident: 1308_CR181 doi: 10.1140/epjc/s10052-021-09749-7 – ident: 1308_CR71 doi: 10.1016/j.nima.2004.12.018 – ident: 1308_CR16 doi: 10.1103/PhysRevD.84.095006 – ident: 1308_CR26 doi: 10.1007/JHEP03(2021)211 – ident: 1308_CR157 doi: 10.1140/epjc/s10052-019-6800-x – year: 2015 ident: 1308_CR65 publication-title: PLoS One doi: 10.1371/journal.pone.0118432 – volume: 09 start-page: 119 year: 2013 ident: 1308_CR178 publication-title: JHEP doi: 10.1007/JHEP09(2013)119 – ident: 1308_CR138 doi: 10.1007/JHEP07(2019)142 – volume: 83 year: 2011 ident: 1308_CR148 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.83.115019 – ident: 1308_CR9 doi: 10.1103/PhysRevD.95.075025 – volume: 02 start-page: 118 year: 2015 ident: 1308_CR100 publication-title: JHEP doi: 10.1007/JHEP02(2015)118 – volume: 68 start-page: 161 year: 2018 ident: 1308_CR45 publication-title: Ann. Rev. Nucl. Part. Sci. doi: 10.1146/annurev-nucl-101917-021019 – volume: 03 start-page: 012 year: 2011 ident: 1308_CR88 publication-title: JHEP doi: 10.1007/JHEP03(2011)012 – ident: 1308_CR28 doi: 10.1007/JHEP10(2021)063 – ident: 1308_CR20 doi: 10.1007/JHEP09(2021)080 – ident: 1308_CR72 doi: 10.1016/j.nima.2005.09.022 – volume: 102 year: 2020 ident: 1308_CR147 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.102.015031 – volume: 15 start-page: P12012 year: 2020 ident: 1308_CR107 publication-title: JINST doi: 10.1088/1748-0221/15/12/P12012 – ident: 1308_CR127 doi: 10.1515/9781400881970-018 – volume: 109 year: 2024 ident: 1308_CR159 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.109.035001 – ident: 1308_CR12 doi: 10.1103/PhysRevLett.131.011802 – ident: 1308_CR15 doi: 10.1007/JHEP07(2014)019 – volume-title: The Elements of Statistical Learning: Data Mining, Inference, and Prediction year: 2009 ident: 1308_CR57 doi: 10.1007/978-0-387-84858-7 – volume: 54 start-page: 1937 year: 2021 ident: 1308_CR61 publication-title: Artif. Intell. Rev. doi: 10.1007/s10462-020-09896-5 – volume: 25 start-page: 800 year: 2012 ident: 1308_CR173 publication-title: Phys. Procedia doi: 10.1016/j.phpro.2012.03.160 – ident: 1308_CR63 doi: 10.1007/0-387-25465-X_40 – ident: 1308_CR124 doi: 10.1007/JHEP04(2021)174 – ident: 1308_CR17 doi: 10.1007/JHEP06(2012)006 – ident: 1308_CR125 doi: 10.1007/JHEP06(2023)031 – ident: 1308_CR176 – ident: 1308_CR34 doi: 10.1103/PhysRevD.108.035043 – ident: 1308_CR24 doi: 10.1007/JHEP05(2017)072 – ident: 1308_CR117 – ident: 1308_CR53 – ident: 1308_CR109 – ident: 1308_CR78 – ident: 1308_CR182 – ident: 1308_CR47 – volume: 73 start-page: 2677 year: 2013 ident: 1308_CR122 publication-title: Eur. Phys. J. C doi: 10.1140/epjc/s10052-013-2677-2 – ident: 1308_CR7 doi: 10.1007/JHEP01(2016)051 – ident: 1308_CR126 – volume: 74 start-page: 3076 year: 2014 ident: 1308_CR39 publication-title: Eur. Phys. J. C doi: 10.1140/epjc/s10052-014-3076-z – ident: 1308_CR93 doi: 10.21468/SciPostPhys.7.6.075 – volume: 69 year: 2004 ident: 1308_CR141 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.69.093002 – ident: 1308_CR85 doi: 10.1140/epjc/s10052-017-5196-8 – volume: 73 start-page: 2650 year: 2013 ident: 1308_CR133 publication-title: Eur. Phys. J. C doi: 10.1140/epjc/s10052-013-2650-0 – ident: 1308_CR70 – volume: 20 start-page: 1093 year: 2006 ident: 1308_CR175 publication-title: Int. J. Pattern Recognit. Artif. Intell. doi: 10.1142/S0218001406005137 – ident: 1308_CR154 – ident: 1308_CR120 doi: 10.1007/JHEP06(2023)060 – volume: 91 year: 2019 ident: 1308_CR48 publication-title: Rev. Mod. Phys. doi: 10.1103/RevModPhys.91.045002 – volume: 73 start-page: 2480 year: 2013 ident: 1308_CR185 publication-title: Eur. Phys. J. C doi: 10.1140/epjc/s10052-013-2480-0 |
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