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Search for radiative leptonic decay D + →γe + ν e using deep learning

Title: Search for radiative leptonic decay D + →γe + ν e using deep learning
Authors: Ablikim, M.; Achasov, M.N.; Adlarson, P.; Ai, X.C.; Aliberti, R.; Amoroso, A.; Kalantar-Nayestanaki, Nasser; Kavatsyuk, Myroslav; Messchendorp, Johan
Source: BESIII Collaboration, Ablikim, M, Achasov, M N, Adlarson, P, Ai, X C, Aliberti, R, Amoroso, A, Kalantar-Nayestanaki, N, Kavatsyuk, M & Messchendorp, J 2025, 'Search for radiative leptonic decay D + →γe + ν e using deep learning', Chinese Physics C, vol. 49, no. 8, 083001. https://doi.org/10.1088/1674-1137/adcdf3
Publication Year: 2025
Collection: University of Groningen research database
Description: Using 20.3 fb-1 of annihilation data collected at a center-of-mass energy of 3.773 GeV with the BESIII detector, we report on an improved search for the radiative leptonic decay (Formula presented). An upper limit on its partial branching fraction for photon energies Ey >10 MeV was determined to be 1.2*10-5 at a 90% confidence level; this excludes most current theoretical predictions. A sophisticated deep learning approach, which includes thorough validation and is based on the Transformer architecture, was implemented to efficiently distinguish the signal from massive backgrounds.
Document Type: article in journal/newspaper
File Description: application/pdf
Language: English
ISSN: 1674-1137
Relation: info:eu-repo/semantics/altIdentifier/hdl/https://hdl.handle.net/11370/cb3c3c1a-f256-4385-9124-46823dffd5e0; info:eu-repo/semantics/altIdentifier/pissn/1674-1137
DOI: 10.1088/1674-1137/adcdf3
Availability: https://hdl.handle.net/11370/cb3c3c1a-f256-4385-9124-46823dffd5e0; https://research.rug.nl/en/publications/cb3c3c1a-f256-4385-9124-46823dffd5e0; https://doi.org/10.1088/1674-1137/adcdf3; https://pure.rug.nl/ws/files/1417490047/Ablikim_2025_Chinese_Phys._C_49_083001.pdf
Rights: info:eu-repo/semantics/openAccess ; http://creativecommons.org/licenses/by/4.0/
Accession Number: edsbas.F384E70E
Database: BASE