255 citations · 401 across the 4 of their papers we have counts for
4 papers
Machine Learning in Nuclear Physics
Amber Boehnlein, Markus Diefenthaler, Cristiano Fanelli +15
Advances in machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of nuclear physics…
A survey of machine learning-based physics event generation
Yasir Alanazi, N. Sato, Pawel Ambrozewicz +5
Event generators in high-energy nuclear and particle physics play an important role in facilitating studies of particle reactions. We survey the state-of-the-art of machine learnin…
Simulation of electron-proton scattering events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)
Yasir Alanazi, N. Sato, Tianbo Liu +9
We apply generative adversarial network (GAN) technology to build an event generator that simulates particle production in electron-proton scattering that is free of theoretical as…
Bayesian Reweighting for Global Fits
Nobuo Sato, J. F. Owens, Harrison Prosper
Two different techniques for adding additional data sets to existing global fits using Bayesian reweighting have been proposed in the literature. The derivation of each reweighting…