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New graph-neural-network flavor tagger for Belle II and measurement of sin <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mn>2</mml:mn><mml:msub><mml:mi>ϕ</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math> in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msup><mml:mi>B</mml:mi><mml:mn>0</mml:mn></mml:msup><mml:mo stretchy="false">→</mml:mo><mml:mi>J</mml:mi><mml:mo>/</mml:mo><mml:mi>ψ</mml:mi><mml:msubsup><mml:mi>K</mml:mi><mml:mi mathvariant="normal">S</mml:mi><mml:mn>0</mml:mn></mml:msubsup></mml:math> decays
I. Adachi, L. Aggarwal, H. Ahmed, H. Aihara, Н. Акопов, A. Aloisio, N. Anh Ky, D. M. Asner, T. Aushev, V. Aushev, M. Aversano, R. Ayad, V. Babu, H. Bae, S. Bahinipati, P. Bambade, Sw. Banerjee, S. Bansal, M. Barrett, J. Baudot, A. Baur, A. Beaubien, F. Becherer, J. Becker, J. V. Bennett, F. U. Bernlochner, V. Bertacchi, M. Bertemes, E. Bertholet, M. Bessner, S. Bettarini, B. Bhuyan, F. Bianchi, L. Bierwirth, T. Bilka, S. Bilokin, D. Biswas, A. Bobrov, D. Bodrov, A. E. Bolz, A. Bondar, A. Bożek, M. Bračko, P. Branchini, R. A. Briere, T. E. Browder, A. Budano, S. Bussino, M. Campajola, L. Cao, G. Casarosa, C. Cecchi, J. Cerasoli, P. Chang, P. Cheema, C. Chen, B. G. Cheon, K. Chilikin, K. Chirapatpimol, K. Cho, S. -J. Cho, S. Choudhury, J. Cochran, L. Corona, F. Dattola, E. De La Cruz–Burelo, S. A. De La Motte, G. De Nardo, M. De Nuccio, G. De Pietro, R. de Sangro, M. Destefanis, S. Dey, R. Dhamija, A. Di Canto, Z. Doležal, T. V. Dong, M. Dorigo, K. Dort, D. Dossett, S. Dreyer, S. Dubey, G. Dujany, P. Ecker, M. Eliachevitch, P. Feichtinger, T. Ferber, D. Ferlewicz, T. Fillinger, C. Finck, G. Finocchiaro, A. Fodor, F. Forti, A. Frey, B. G. Fulsom, A. Gabrielli, E. Ganiev, M. Garcia-Hernandez, G. Gaudino, V. Gaur, A. Gaz, A. Gellrich, G. Ghevondyan, D. Ghosh, H. Ghumaryan, G. Giakoustidis, R. Giordano, A. Giri, A. Glazov, B. Gobbo, R. Godang, O. Gogota, P. Goldenzweig, W. Gradl, T. Grammatico, E. Graziani, D. Greenwald, Z. Gruberová, T. Gu, Y. Guan, K. Gudkova, K. Hara, T. Hara, K. Hayasaka, H. Hayashii, S. Hazra, C. Hearty, M. T. Hedges, A. Heidelbach, I. Heredia-De La Cruz, M. Hernández Villanueva, T. Higuchi, M. Hohmann, P. Horak, T. Humair, T. Iijima, K. Inami, N. Ipsita, A. Ishikawa, R. Itoh, M. Iwasaki, P. Jackson, W. W. Jacobs, D. E. Jaffe, Q. P. Ji, S. Jia, K. K. Joo, H. Junkerkalefeld, H. Kakuno, D. Kalita, J. Kandra, K. H. Kang, S. Kang, G. Karyan, T. Kawasaki, F. Keil, C. Kiesling, D. Y. Kim, K. -H. Kim, H. Kindo, K. Kinoshita, P. Kodyš, T. Koga, S. Kohani, K. Kojima, A. Korobov, S. Korpar, E. Kovalenko, R. Kowalewski, T. M. G. Kraetzschmar, P. Križan, P. Krokovny, T. Kuhr, Y. Kulii, J. Kumar, M. Kumar, R. Kumar, K. Kumara, T. Kunigo, A. Kuzmin, S. Lacaprara, T. Lam, L. Lanceri, J. S. Lange, M. Laurenza, R. Leboucher, M. J. Lee, D. Levit, C. Li, L. K. Li, Y. Li, Y. B. Li, J. Libby, Q. Y. Liu, Zhiqing Liu, D. Liventsev, S. Longo, T. Lueck, C. Lyu, M. Maggiora, S. P. Maharana, R. Maiti, S. Maity, G. Mancinelli, R. Manfredi, E. Manoni, M. Mantovano, D. Marcantonio, S. Marcello, C. Mariñas, L. Martel, C. Martellini, A. Martini, T. Martinov, L. Massaccesi, M. Masuda, K. Matsuoka, D. Matvienko, S. K. Maurya, J. A. McKenna, R. Mehta, F. Meier, M. Merola, F. Metzner, C. Miller, M. Mirra, K. Miyabayashi, H. Miyake, R. Mizuk, G. B. Mohanty, N. Molina-Gonzalez, S. Mondal, S. Moneta, M. Mrvar, R. Mussa, I. Nakamura, K. R. Nakamura, M. Nakao, Y. Nakazawa, A. Narimani Charan, M. Naruki, D. Narwal, Z. Natkaniec, A. Natochii, L. Nayak, M. Nayak, Mary P. Neu, C. Niebuhr, S. Nishida, S. Ogawa, Y. Onishchuk, H. Ono, Yoshichika Ōnuki, P. Oskin, F. Otani, P. Pakhlov, G. Pakhlova, A. Panta, S. Pardi, K. Parham, H. Park, B. Paschen, A. Passeri, S. Patra, S. Paul, T. K. Pedlar, R. Peschke, R. Pestotnik, M. Piccolo, L. E. Piilonen, G. Pinna Angioni, P. L. M. Podesta-Lerma, T. Podobnik, S. Pokharel, C. Praz, S. Prell, E. Prencipe, M. T. Prim, H. Purwar, P. Rados, G. Raeuber, S. Raiz, N. Rauls, M. Reif, S. Reiter, M. Remnev, I. Ripp-Baudot, G. Rizzo, M. Röhrken, J. M. Roney, A. Rostomyan, N. Rout, G. Russo, D. A. Sanders, S. Sandilya, A. Sangal, L. Šantelj, Y. Sato, V. Savinov, B. Scavino, C. Schmitt, C. Schwanda, Y. Seino, A. Selce, K. Senyo, J. Serrano, M. E. Sevior, C. Sfienti, X. Shi, T. Shillington, T. Shimasaki, D. Shtol, A. Sibidanov, F. Simon, J. B. Singh, J. Skorupa, R. Sobie, M. Sobotzik, A. Soffer, A. Sokolov, E. Solovieva, S. Spataro, B. Spruck, M. Starič, P. Stavroulakis, S. Stefkova, R. Stroili, K. Sumisawa, W. Sutcliffe, H. Svidras, M. Takizawa, U. Tamponi, K. Tanida, F. Tenchini, O. Tittel, R. Tiwary, D. Tonelli, E. Torassa, K. Trabelsi, I. Tsaklidis, M. Uchida, I. Ueda, Y. Uematsu, Y. Unno, K. Uno, S. Uno, P. Urquijo, Y. Ushiroda, S. Vahsen, R. van Tonder, K. E. Varvell, M. Veronesi, A. Vinokurova, V. S. Vismaya, L. Vitale, V. Vobbilisetti, R. Volpe, B. Wach, M. Wakai, S. Wallner, E. Wang, M.-Z. Wang, Z. Wang, A. Warburton, S. Watanuki, C. Wessel, E. Won, B. Yabsley, S. Yamada, W. Yan, S. B. Yang, J. Yelton, J. H. Yin, K. Yoshihara, C. Z. Yuan, Y. Yusa, B. Zhang, V. Zhilich, Q. Zhou, V. Zhukova, R. Žlebčík
Bibliographic record
VenuePhysical review. D/Physical review. D. · 2024
Typepreprint
Languagelv
FieldPhysics and Astronomy
TopicParticle physics theoretical and experimental studies
Canadian institutionsnot available
FundersHORIZON EUROPE Marie Sklodowska-Curie ActionsInstitut National de Physique Nucléaire et de Physique des ParticulesAkademi Sains MalaysiaInstituto Nazionale di Fisica NucleareJapan Society for the Promotion of ScienceAustralian Research CouncilMinistry of Education, Culture, Sports, Science and TechnologyBundesministerium für Bildung und ForschungNatural Sciences and Engineering Research Council of CanadaHorizon 2020 Framework ProgrammeKementerian Pendidikan MalaysiaBrookhaven National LaboratoryMax-Planck-GesellschaftCentre National de la Recherche ScientifiqueEuropean Research CouncilMinistry of Education and Science of UkraineMinisterstwo Edukacji i NaukiNational Key Research and Development Program of ChinaIsrael Science FoundationNational Natural Science Foundation of ChinaNational Research Foundation of KoreaU.S. Department of EnergyAlexander von Humboldt-StiftungKorea Institute of Science and Technology InformationDepartment of Atomic Energy, Government of IndiaAgence Nationale de la RechercheNational Institute of InformaticsAustrian Science FundGrantová Agentura České RepublikyUniversiti MalayaCentral University Basic Research Fund of ChinaIstituto Nazionale di Fisica NucleareMinistry of EducationDepartment of Science and Technology, Ministry of Science and Technology, IndiaNational Research FoundationBundesministerium für Bildung, Wissenschaft und ForschungDeutsche ForschungsgemeinschaftNational Research Foundation of UkraineDeutsches Elektronen-SynchrotronGeneralitat ValencianaHelmholtz-GemeinschaftIstituto Nazionale di Fisica Nucleare Sezione di PadovaNatural Science Foundation of Shandong ProvinceCompute CanadaUniversity of TabukVietnam Academy of Science and TechnologyMinistry of Science and Higher Education of the Russian FederationAgencia Estatal de InvestigaciónUnited States - Israel Binational Agricultural Research and Development FundMinistry of Education and ScienceCanarieNational Science FoundationInstitute of Energy, Hefei Comprehensive National Science CenterNational Science and Technology Council
KeywordsGraphPhysicsParticle physicsNuclear physicsComputer scienceTheoretical computer science
Abstract
We present GFlaT, a new algorithm that uses a graph-neural-network to determine the flavor of neutral B mesons produced in ϒ(4S) decays. It improves previous algorithms by using the information from all charged final-state particles and the relations between them. We evaluate its performance using B decays to flavor-specific hadronic final states reconstructed in a 362 fb−1 sample of electron-positron collisions collected at the ϒ(4S) resonance with the Belle II detector at the SuperKEKB collider. We achieve an effective tagging efficiency of (37.40±0.43±0.36%) , where the first uncertainty is statistical and the second systematic, which is 18% better than the previous Belle II algorithm. Demonstrating the algorithm, we use B0→J/ψKS0 decays to measure the mixing-induced and direct CP violation parameters, S=(0.724±0.035±0.009) and C=(−0.035±0.026±0.029) . Published by the American Physical Society 2024
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.044
Threshold uncertainty score0.147
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.026 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
Opus teacher head0.019
GPT teacher head0.288
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".
Quick stats
Citations13
Published2024
Admission routes1
Has abstractyes