28 citations · 55 across the 7 of their papers we have counts for
7 papers
Machine Learning Unveils Finite-volume Energy Shifts in Three-body System
Wei-Jie Zhang, Zhenyu Zhang, Jifeng Hu +3
Finite-volume extrapolation (FVE) is essential for extracting physical observables in the lattice calculation. While rigorous FVE formulations are well established for short-range…
Machine Learning Unveils the Power Law of Finite-Volume Energy Shifts
Wei-Jie Zhang, Zhenyu Zhang, Jifeng Hu +3
Finite-volume extrapolation is an important step for extracting physical observables from lattice calculations. However, it is a significant challenge for the system with long-rang…
Binding of the three-hadron system from the lattice effective field theory
Zhenyu Zhang, Xin-Yue Hu, Guangzhao He +4
We employ the nuclear lattice effective field theory (NLEFT), an efficient tool for nuclear ab initio calculations, to solve the asymmetric multihadron systems. We take the …
Revealing the nature of hidden charm pentaquarks with machine learning
Zhenyu Zhang, Jiahao Liu, Jifeng Hu +2
We study the nature of the hidden charm pentaquarks, i.e. the , and , with a neural network approach in pionless effective field theory. In this f…
The third peak structure in the double spectrum
PengYu Niu, Zhenyu Zhang, Qian Wang +1
Recently, the CMS and ATLAS collaborations have reported their and invariant mass distributions, respectively, for searching for fully charmed tetraquarks. B…
Discover the GellMann-Okubo formula with machine learning
Zhenyu Zhang, Rui Ma, Jifeng Hu +1
Machine learning is a novel and powerful technology and has been widely used in various science topics. We demonstrate a machine-learning based approach built by a set of general m…
Study of exotic hadrons with machine learning
Jiahao Liu, Zhenyu Zhang, Jifeng Hu +1
We analyzed the invariant mass spectrum of near-threshold exotic states for one-channel candidates with a deep neural network. It can extract the scattering length and effective ra…