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【学术活动03.26】Machine learning enhanced Multi-messenger Probes for New Physics in the LHAASO experiment

  Title: Machine learning enhanced Multi-messenger Probes for New Physics in the LHAASO experiment 

  Speaker: Dr. Antonino Marciano (Fudan University and INFN Frascati) 

  Host: Dr. Quanbu Gou 

  Time: 10:00 (Beijing Time), Friday, 2021-3-26 

  Place: 226 meeting room, Multidisciplinary Building, IHEP, CAS 

  Tencent meeting:  https://meeting.tencent.com/s/g4ocRiSuakNe 

  Tencent meeting id: 385 686 802 



  The possibility to probe for the existence of dark matter in a multi-messenger approach, thanks to the recent discovery of gravitational waves, is nowadays a cutting-edge research topic. Understanding the nature and origin of dark matter, and answering other fundamental questions in particle physics, including the mass generation of neutrinos, the nature of confinement in QCD, the hierarchy problem among the values of the coupling constants and their relation to the mass of the Higgs particle, the nature of dark energy and of the inflation field, just to mention a few of them, would definitely represent a tremendous achievement in physics. Here we propose a multi-messenger approach for the analysis of dark matter models and the tentative answer of these fundamental questions. We aim at extracting information on the properties and interactions of dark matter, and finally on its genesis, combining multi-messenger astronomy techniques and inputs from laboratory physics. The main objectives planned by the collaboration comprise: i) the multi-messenger analysis of new physics, including mainly, but not only, several different models of dark matter; ii) the phenomenology of new physics signatures in LHAASO, with cross-correlation to the corresponding physical, astrophysical and cosmological observations; iii) the development of machine learning methods for LHAASO data analysis, in light of the new physics signatures. Together with the comparative analysis of gamma rays and cosmic rays’ spectra empowered by using machine learning techniques, an important and innovative strength of our planned activities concerns the possibility to test alternative models of dark matter. We foresee that a cross-fertilizing approach combining the information that arise from so different experimental methodologies will represent the right and successful path to extract information about the very elusive dark matter particles and provide answers to the main questions that are left in fundamental physics. 


  About the speaker:  

  Antonino Marciano’ is currently Tenured Associate Professor at Fudan University, Full Professor for the Italian Ministry of University and Research, and member of the Italian Institute of Nuclear Physics (INFN), assigned to the theory division of the Frascati laboratories.  

  As a theoretical physicist, Antonino focuses his scientific research on a variety of topics, ranging mainly from quantum field theories and theories of gravity, to their neighboring research areas (including also cosmic rays physics and dark matter models), as well as analogous applications to Solid State Physics and Artificial Intelligence.  

  Antonino has published so far about 100 articles in international scientific journals with the highest impact factors. He serves as referee for Nature, the American Physical Society, and several Chinese and European journals to assess publications of scientific papers, and as a reviewer for the Italian Minister of the University and Research, for the Dutch Royal Academy of Science, and for the National Science Centre of Poland.