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TG2.1: High energy phenomenology
I. Coordinator:Meng-Ru Wu 吳孟儒 (AS)
mwu [at] as.edu.tw
II. Core Members:
Center Scientists
Meng-Ru Wu 吳孟儒 (AS)
Cheng-Wei Chiang 蔣正偉 (NTU)
Chia-Hsien Shen 沈家賢 (NTU)
Po-Yen Tseng 曾柏彥 (NTHU)
Core members
Chian-Shu Chen 陳樫旭 (TKU)
Chuan-Hung Chen 陳泉宏 (NCKU)
Chuan-Ren Chen 陳傳仁 (NTNU)
Kingman Cheung 張敬民 (NTHU)
Sho Iwamoto 岩本 祥 (NSYSU)
Guey-Lin Lin 林貴林 (NYCU)
Kin-Wang Ng 吳建宏 (AS)
Postdocs
Tran, Van Que 陳文桂
Ngo, Phuc Duc Loc 吳福德祿
III. Research Themes:
- Cosmology
- Deep machine learning
- Effective field theory
- Flavor physics
- Gravitational wave detectors as particle physics probes
- Higgs physics
- Neutrino physics
- Particle astrophysics
IV. Activities
V. Expected achievements:
In the next few years, we expect significant research progress in all the above-mentioned directions. Our core members will strive to propose novel methods to help experimentalists making precision measurements of the Higgs couplings and probing direct searches of exotic Higgs bosons. The new flavor data will push our group members to examine whether there are indeed hints of new physics, as the current data suggest, and what they imply. Neutrino physics provides a steady flow of new results which will be closely followed and accompanied by research papers by NCTS scientists and their groups. Close connections of center scientists with the local and international gravitational wave community and the development of experimental infrastructure in Taiwan itself will also allow unique opportunities in this exciting new field. Exploration of exciting and new astronomical observations on implications to particle physics and cosmology will be made. The core members will apply modern techniques of effective field theory not only to particle phenomenology, but also to gravitational waves and cosmology. This will set the stage for the researchers in Taiwan for modern challenges and opportunities in the new era. Finally, as an emerging new tool in theoretical physics analyses, the TG members will, in collaboration with the experimental colleagues whenever possible, devise better deep neural networks to help improve efficiencies in various aspects of particle physics studies.