255 citations · 497 across the 19 of their papers we have counts for
3 papers · 1 filter
Machine learning-based event generator for electron-proton scattering
Y. Alanazi, P. Ambrozewicz, M. Battaglieri +11
We present a new machine learning-based Monte Carlo event generator using generative adversarial networks (GANs) that can be trained with calibrated detector simulations to constru…
Bayesian Neural Networks for Fast SUSY Predictions
Braden Kronheim, Michelle Kuchera, Harrison Prosper +1
One of the goals of current particle physics research is to obtain evidence for new physics, that is, physics beyond the Standard Model (BSM), at accelerators such as the Large Had…
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…