Sensitivity of the SHiP Experiment to Dark Photons Decaying to a Pair of Charged Particles
European Physical Journal C(2021)SCI 2区
European Organization for Nuclear Research (CERN) | Middle East Technical University (METU) | Sezione INFN di Napoli | Skobeltsyn Institute of Nuclear Physics of Moscow State University (SINP MSU) | Kobe University | National Research Nuclear University (MEPhI) | Joint Institute for Nuclear Research (JINR) | University of Warwick | P.N. Lebedev Physical Institute (LPI RAS) | Yandex School of Data Analysis | École Polytechnique Fédérale de Lausanne (EPFL) | STFC Rutherford Appleton Laboratory | Laboratori Nazionali dell’INFN di Frascati | St. Petersburg Polytechnic University (SPbPU) | Universität Zürich | Taras Shevchenko National University of Kyiv | Universität Hamburg | LIP | Sofia University | University of Copenhagen | University of Leiden | Sezione INFN di Cagliari | Uppsala University | Université Paris-Saclay | Johannes Gutenberg Universität Mainz | University College London | Sorbonne Université | Sungkyunkwan University | Universidad Técnica Federico Santa María and Centro Científico Tecnológico de Valparaíso | Sezione INFN di Bologna | Sezione INFN di Bari | Universität Bonn | Laboratori Nazionali dell’INFN di Gran Sasso | National University of Science and Technology “MISiS” | National Research Centre “Kurchatov Institute” | Institute for High Energy Physics (IHEP) NRC “Kurchatov Institute” | University of Geneva | Humboldt-Universität zu Berlin | Petersburg Nuclear Physics Institute (PNPI) NRC “Kurchatov Institute” | Imperial College London | Nagoya University | Institute of Theoretical and Experimental Physics (ITEP) NRC “Kurchatov Institute” | Institute for Nuclear Research of the Russian Academy of Sciences (INR RAS) | University of Belgrade | Ankara University | Gyeongsang National University | Aichi University of Education | Toho University | Korea University | University of Bristol | Nihon University | Stockholm University | Forschungszentrum Jülich GmbH (KFA)
- 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

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