H
Huey-Wen Lin
Researcher at Michigan State University
Publications - 237
Citations - 8961
Huey-Wen Lin is an academic researcher from Michigan State University. The author has contributed to research in topics: Lattice QCD & Nucleon. The author has an hindex of 48, co-authored 213 publications receiving 7493 citations. Previous affiliations of Huey-Wen Lin include Columbia University & Thomas Jefferson National Accelerator Facility.
Papers
More filters
Journal ArticleDOI
Gaussian-weighted Parton Quasi-distribution
Jiunn-Wei Chen,Tomomi Ishikawa,Luchang Jin,Huey-Wen Lin,Andreas Schäfer,Yi-Bo Yang,Jian-Hui Zhang,Yong Zhao +7 more
TL;DR: In this paper, a revised definition of quasi-distributions is proposed within the framework of large-momentum effective theory (LaMET) that improves convergence towards the large-Momentum limit.
Journal ArticleDOI
Nuclear σ terms and scalar-isoscalar WIMP-nucleus interactions from lattice QCD
TL;DR: In this paper, the authors used lattice QCD calculations of the binding energies of the deuteron, He-3 and He-4 at pion masses near 500 MeV and 800 MeV, combined with the experimentally determined binding energies at the physical point, to provide approximate determinations of the \sigma-terms for these light nuclei.
Journal ArticleDOI
Lattice study of the N − P 11 transition form factors
TL;DR: In this paper, a model-independent study of the Roper-nucleon transition form factor was performed using first-principles lattice QCD for the first time.
Journal ArticleDOI
The QCDOC Project
Peter Boyle,Peter Boyle,Dong Chen,Norman H. Christ,Michael A. Clark,Saul D. Cohen,C. Cristian,Z. Dong,Alan Gara,Balint Joo,C. K. Jung,C. Kim,Ludmila Levkova,X. Liao,G. Liu,S. Li,Huey-Wen Lin,Robert D. Mawhinney,S. Ohta,K. Petrov,Tilo Wettig,Tilo Wettig,Azusa Yamaguchi,Azusa Yamaguchi +23 more
TL;DR: The QCDOC project has developed a supercomputer optimised for the needs of Lattice QCD simulations that provides a very competitive price to sustained performance ratio of around $1 USD per sustained Megaflop/s in combination with outstanding scalability.
Journal ArticleDOI
Machine-learning prediction for quasiparton distribution function matrix elements
TL;DR: In this article, the authors explore whether machine-learning algorithms can make predictions of correlators to reduce the computational cost of lattice QCD calculations, and find that both algorithms can reliably predict the target observables with different prediction accuracy and systematic errors.