Zobrazeno 1 - 10
of 7 610
pro vyhledávání: '"HAN Tao"'
Autor:
Bai, Ai-Yu, Cai, Hanjie, Chen, Chang-Lin, Chen, Siyuan, Chen, Xurong, Chen, Yu, Cheng, Weibin, Dai, Ling-Yun, Fan, Rui-Rui, Gong, Li, Guo, Zihao, He, Yuan, Hou, Zhilong, Huang, Yinyuan, Jia, Huan, Jiang, Hao, Jing, Han-Tao, Kang, Xiaoshen, Li, Hai-Bo, Li, Jincheng, Li, Yang, Liu, Shulin, Lu, Guihao, Miao, Han, Ning, Yunsong, Niu, Jianwei, Peng, Huaxing, Petrov, Alexey A., Qin, Yuanshuai, Sun, Mingchen, Tang, Jian, Tang, Jing-Yu, Tian, Ye, Wang, Rong, Wang, Xiaodong, Wang, Zhichao, Wu, Chen, Xing, Tian-Yu, Xiong, Weizhi, Xu, Yu, Yan, Baojun, Yao, De-Liang, Yu, Tao, Yuan, Ye, Yuan, Yi, Zhang, Yao, Zhang, Yongchao, Zhang, Zhilv, Zhao, Guang, Zhao, Shihan
The spontaneous conversion of muonium to antimuonium is one of the interesting charged lepton flavor violation phenomena, offering a sensitive probe of potential new physics and serving as a tool to constrain the parameter space beyond the Standard M
Externí odkaz:
http://arxiv.org/abs/2410.18817
We study the sensitivity reach to probe the electroweak dipole operators associated with a top quark at a multi-TeV lepton collider. Studying the electroweak dipole operators is strongly motivated by precision physics. The operators exhibit unique ch
Externí odkaz:
http://arxiv.org/abs/2410.11015
The interpretation of groups of particle spins at colliders as quantum states has opened up the possibility of using colliders for quantum information. While most efforts have focused on utilizing the decays of the particles to infer their spins to r
Externí odkaz:
http://arxiv.org/abs/2410.08303
Autor:
Ma, Yang, Celada, Eugenia, Han, Tao, Kilian, Wolfgang, Kreher, Nils, Maltoni, Fabio, Pagani, Davide, Reuter, Jürgen, Striegl, Tobias, Xie, Keping
We establish a simple yet general parameterization of Higgs-muon interactions within the effective field theory frameworks, including both the Higgs Effective Field Theory (HEFT) and the Standard Model Effective Field Theory (SMEFT). We investigate t
Externí odkaz:
http://arxiv.org/abs/2410.06991
Autor:
Qin, Ying, Wang, Zhen-Han-Tao, Meynet, Georges, Hu, Rui-Chong, Fu, Chengjie, Shu, Xin-Wen, Wang, Zi-Yuan, Yi, Shuang-Xi, Tang, Qing-Wen, Song, Han-Feng, Liang, En-Wei
During the fourth observing run, the LIGO-Virgo-KAGRA Collaboration reported the detection of a coalescing compact binary (GW230529$_{-}$181500) with component masses estimated at $2.5-4.5\, M_\odot$ and $1.2-2.0\, M_\odot$ with 90\% credibility. Giv
Externí odkaz:
http://arxiv.org/abs/2409.14476
Numerical Weather Prediction (NWP) system is an infrastructure that exerts considerable impacts on modern society.Traditional NWP system, however, resolves it by solving complex partial differential equations with a huge computing cluster, resulting
Externí odkaz:
http://arxiv.org/abs/2409.16321
Autor:
Qin, Ying, Zhu, Jin-Ping, Meynet, Georges, Zhang, Bing, Wang, Fa-Yin, Shu, Xin-Wen, Song, Han-Feng, Wang, Yuan-Zhu, Yuan, Liang, Wang, Zhen-Han-Tao, Hu, Rui-Chong, Wu, Dong-Hong, Yi, Shuang-Xi, Tang, Qing-Wen, Wei, Jun-Jie, Wu, Xue-Feng, Liang, En-Wei
On April 25th, 2019, the LIGO-Virgo Collaboration discovered a Gravitational-wave (GW) signal from a binary neutron star (BNS) merger, i.e., GW190425. Due to the inferred large total mass, the origin of GW190425 remains unclear. We perform detailed s
Externí odkaz:
http://arxiv.org/abs/2409.10869
Sterile neutrinos can influence the evolution of the universe, and thus cosmological observations can be used to detect them. Future gravitational wave (GW) observations can precisely measure absolute cosmological distances, helping to break paramete
Externí odkaz:
http://arxiv.org/abs/2409.04453
Recent advancements in deep learning (DL) have led to the development of several Large Weather Models (LWMs) that rival state-of-the-art (SOTA) numerical weather prediction (NWP) systems. Up to now, these models still rely on traditional NWP-generate
Externí odkaz:
http://arxiv.org/abs/2408.11438
Existing truth inference methods in crowdsourcing aim to map redundant labels and items to the ground truth. They treat the ground truth as hidden variables and use statistical or deep learning-based worker behavior models to infer the ground truth.
Externí odkaz:
http://arxiv.org/abs/2407.13268