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Электронный каталог: Ablikim, M. - Search for Radiative Leptonic Decay D*+ *> *ge*+*n&sub(e) Using Deep Learning
Ablikim, M. - Search for Radiative Leptonic Decay D*+ *> *ge*+*n&sub(e) Using Deep Learning
Статья
Автор: Ablikim, M.
Chinese Physics C: Search for Radiative Leptonic Decay D*+ *> *ge*+*n&sub(e) Using Deep Learning
б.г.
ISBN отсутствует
Автор: Ablikim, M.
Chinese Physics C: Search for Radiative Leptonic Decay D*+ *> *ge*+*n&sub(e) Using Deep Learning
б.г.
ISBN отсутствует
На полку
Статья
Ablikim, M.
Search for Radiative Leptonic Decay D*+ *> *ge*+*n&sub(e) Using Deep Learning / M.Ablikim, O.Bakina, I.Boyko, G.Chelkov, D.Dedovich, I.Denysenko, P.Egorov, A.Guskov, Y.Nefedov, A.Sarantsev, A.Zhemchugov, [a.o.]. – Text : electtronic // Chinese Physics C. – 2025. – Vol. 49, No. 8. – P. 083001. – URL: https://doi.org/10.1088/1674-1137/adcdf3. – Bibliogr.: 60.
Using 20.3 fb&sub(–1) of e*+e*- annihilation data collected at a center-of-mass energy of 3.773 D*+ *>*ge*+*n&sub(e) GeV with the BESIII detector, we report on an improved search for the radiative leptonic decay . An upper limit on its partial branching fraction for photon energies E&sub(*g) > 10 MeV MeV was determined to be 1.2 x10*-*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.
ОИЯИ = ОИЯИ (JINR)2025
Ablikim, M.
Search for Radiative Leptonic Decay D*+ *> *ge*+*n&sub(e) Using Deep Learning / M.Ablikim, O.Bakina, I.Boyko, G.Chelkov, D.Dedovich, I.Denysenko, P.Egorov, A.Guskov, Y.Nefedov, A.Sarantsev, A.Zhemchugov, [a.o.]. – Text : electtronic // Chinese Physics C. – 2025. – Vol. 49, No. 8. – P. 083001. – URL: https://doi.org/10.1088/1674-1137/adcdf3. – Bibliogr.: 60.
Using 20.3 fb&sub(–1) of e*+e*- annihilation data collected at a center-of-mass energy of 3.773 D*+ *>*ge*+*n&sub(e) GeV with the BESIII detector, we report on an improved search for the radiative leptonic decay . An upper limit on its partial branching fraction for photon energies E&sub(*g) > 10 MeV MeV was determined to be 1.2 x10*-*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.
ОИЯИ = ОИЯИ (JINR)2025
