255 citations · 497 across the 19 of their papers we have counts for
7 papers · 1 filter
TensorBNN: Bayesian Inference for Neural Networks using Tensorflow
Braden Kronheim, Michelle Kuchera, Harrison Prosper
TensorBNN is a new package based on TensorFlow that implements Bayesian inference for modern neural network models. The posterior density of neural network model parameters is repr…
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…
Unsupervised Learning for Identifying Events in Active Target Experiments
Robert Solli, Daniel Bazin, Michelle P. Kuchera +2
This article presents novel applications of unsupervised machine learning methods to the problem of event separation in an active target detector, the Active-Target Time Projection…
Report from the A.I. For Nuclear Physics Workshop
Paulo Bedaque, Amber Boehnlein, Mario Cromaz +15
This report is an outcome of the workshop "AI for Nuclear Physics" held at Thomas Jefferson National Accelerator Facility on March 4-6, 2020. The workshop brought together 184 scie…
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…
Modeling Magnetohydrodynamic Equilibrium in Magnetars with Applications to Continuous Gravitational Wave Production
Samuel G. Frederick, Kristen L. Thompson, Michelle P. Kuchera
Possessing the strongest magnetic fields in the Universe, magnetars mark an extremum of physical phenomena. The strength of their magnetic fields is sufficient to deform the shape…
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…