Measurements of the branching fraction ratio B ϕ → μ + μ − / B ϕ → e + e − with charm meson decays
Journal of High Energy Physics(2024)
Nikhef National Institute for Subatomic Physics | Department of Physics | Physik-Institut | Oliver Lodge Laboratory | Instituto Galego de Física de Altas Enerxías (IGFAE) | H.H. Wills Physics Laboratory | Department of Physics and Astronomy | European Organization for Nuclear Research (CERN) | University of Michigan | Université Clermont Auvergne | University of Cincinnati | INFN Laboratori Nazionali di Frascati | Fakultät Physik | School of Physics and Astronomy | ICCUB | Cavendish Laboratory | LPNHE | Universidade Federal do Rio de Janeiro (UFRJ) | Université Paris-Saclay | School of Physics State Key Laboratory of Nuclear Physics and Technology | INFN Sezione di Firenze | INFN Sezione di Ferrara | Syracuse University | INFN Sezione di Milano-Bicocca | University of Chinese Academy of Sciences | INFN Sezione di Roma Tor Vergata | Consejo Nacional de Rectores (CONARE) | Aix Marseille Univ | Massachusetts Institute of Technology | Laboratoire Leprince-Ringuet | Université Savoie Mont Blanc | Physikalisches Institut | Institute of Physics | Centro Brasileiro de Pesquisas Físicas (CBPF) | Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio) | I. Physikalisches Institut | INFN Sezione di Pisa | Institute Of High Energy Physics (IHEP) | INFN Sezione di Genova | DS4DS | Università degli Studi di Padova | Henryk Niewodniczanski Institute of Nuclear Physics Polish Academy of Sciences | Imperial College London | Universiteit Maastricht | University of Maryland | INFN Sezione di Cagliari | Guangdong Provincial Key Laboratory of Nuclear Science | School of Physics and Technology | INFN Sezione di Bologna | INFN Sezione di Milano | Institute for Nuclear Research of the National Academy of Sciences (KINR) | Horia Hulubei National Institute of Physics and Nuclear Engineering | INFN Sezione di Bari | Los Alamos National Laboratory (LANL) | School of Physics and Electronics | Van Swinderen Institute | Institute of Particle Physics | Eotvos Lorand University | Center for High Energy Physics | School of Physics | Tadeusz Kosciuszko Cracow University of Technology | INFN Sezione di Perugia | STFC Rutherford Appleton Laboratory | Universität Bonn - Helmholtz-Institut für Strahlen und Kernphysik | AGH - University of Krakow | National Center for Nuclear Research (NCBJ) | Instituto de Fisica Corpuscular | University of Birmingham | NSC Kharkiv Institute of Physics and Technology (NSC KIPT) | Nikhef National Institute for Subatomic Physics and VU University Amsterdam | Lanzhou University | INFN Sezione di Roma La Sapienza | Departamento de Fisica | Max-Planck-Institut für Kernphysik (MPIK)
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
