Search Results for author:
Found 6 papers, 3 papers with code
Two-dimensional total absorption spectroscopy with conditional generative adversarial networks
We explore the use of machine learning techniques to remove the response of large volume $\gamma$-ray detectors from experimental spectra.
Machine Learning in Nuclear Physics
Advances in machine learning methods provide tools that have broad applicability in scientific research.
Implicit Quantile Neural Networks for Jet Simulation and Correction
Reliable modeling of conditional densities is important for quantitative scientific fields such as particle physics.
Unsupervised Learning for Identifying Events in Active Target Experiments
We also explore the application of clustering the latent space of autoencoder neural networks for event separation.
Simulation of electron-proton scattering events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)
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 assumptions about underlying particle dynamics.
Machine Learning Methods for Track Classification in the AT-TPC
We evaluate machine learning methods for event classification in the Active-Target Time Projection Chamber detector at the National Superconducting Cyclotron Laboratory (NSCL) at Michigan State University.
