1 code implementation • 23 Jun 2022 • Cade Dembski, Michelle P. Kuchera, Sean Liddick, Raghu Ramanujan, Artemis Spyrou
We explore the use of machine learning techniques to remove the response of large volume $\gamma$-ray detectors from experimental spectra.
no code implementations • 4 Dec 2021 • Amber Boehnlein, Markus Diefenthaler, Cristiano Fanelli, Morten Hjorth-Jensen, Tanja Horn, Michelle P. Kuchera, Dean Lee, Witold Nazarewicz, Kostas Orginos, Peter Ostroumov, Long-Gang Pang, Alan Poon, Nobuo Sato, Malachi Schram, Alexander Scheinker, Michael S. Smith, Xin-Nian Wang, Veronique Ziegler
Advances in machine learning methods provide tools that have broad applicability in scientific research.
1 code implementation • 22 Nov 2021 • Braden Kronheim, Michelle P. Kuchera, Harrison B. Prosper, Raghuram Ramanujan
Reliable modeling of conditional densities is important for quantitative scientific fields such as particle physics.
no code implementations • 6 Aug 2020 • Robert Solli, Daniel Bazin, Michelle P. Kuchera, Ryan R. Strauss, Morten Hjorth-Jensen
We also explore the application of clustering the latent space of autoencoder neural networks for event separation.
no code implementations • 29 Jan 2020 • Yasir Alanazi, N. Sato, Tianbo Liu, W. Melnitchouk, Pawel Ambrozewicz, Florian Hauenstein, Michelle P. Kuchera, Evan Pritchard, Michael Robertson, Ryan Strauss, Luisa Velasco, Yaohang Li
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.
2 code implementations • 21 Oct 2018 • Michelle P. Kuchera, Raghuram Ramanujan, Jack Z. Taylor, Ryan R. Strauss, Daniel Bazin, Joshua Bradt, Ruiming Chen
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.