ALPhA Publications
ALPhA members are bold in the author lists below.
Machine learning-based event generator for electron-proton scattering Y. Alanazi, P. Ambrozewicz, M. Battaglieri, A. N. Hiller Blin, M. P. Kuchera, Y. Li, T. Liu, R. E. McClellan, W. Melnitchouk, E. Pritchard, M. Robertson, N. Sato, R. Strauss, and L. Velasco. Phys. Rev. D 106, 096002 (2022).
Machine Learning in Nuclear Physics 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. Reviews of Modern Physics. 94, 031003 (2022).
Implicit Quantile Neural Networks for Jet Simulation and Correction Braden Kronheim, Michelle P. Kuchera, Harrison B. Prosper, Raghuram Ramanujan.NeurIPS Machine Learning and the Physical Sciences workshop. 2021.
Conditional Wasserstein Generative Adversarial Networks for Fast Detector Simulation John Blue, Braden Kronheim, Michelle Kuchera, and Raghuram Ramanujan. 25th International Conference on Computing in High Energy and Nuclear Physics (CHEP). Computational European Physical Journal (EPJ) Web of Conferences 251, 03055 (2021). https://doi.org/10.1051/epjconf/202125103055
TensorBNN: Bayesian Inference for Neural Networks using Tensorflow, B. Kronheim, M.P. Kuchera, H.B. Prosper, Computer Physics Communications 270 (2022) 108168. https://doi.org/10.1016/j.cpc.2021.108168
Simulation of electron-proton scattering events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN), Y. Alanazi, N. Sato, T. Liu, W. Melnitchouk, M. P. Kuchera, E. Pritchard, M. Robertson, R.R. Strauss, L. Velasco, Y. Li. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 2126-2132. 2021. https://doi.org/10.24963/ijcai.2021/293
Unsupervised Learning for Identifying Events in Active Target Experiments R. Solli, D. Bazin, Morten Hjorth-Jensen, M.P. Kuchera, R.R. Strauss, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2021, 165461, ISSN 0168-9002, https://doi.org/10.1016/j.nima.2021.165461.
Variational Autoencoder Inverse Mapper: An End-to-End Deep Learning Framework for Inverse Problems M. Almaeen, Y. Alanazi, N. Sato, W. Melnitchouk,M. P. Kuchera and Y. Li. 2021 International Joint Conference on Neural Networks (IJCNN), 2021, pp. 1-8, doi: 10.1109/IJCNN52387.2021.9534012
Modeling Magnetohydrodynamic Equilibrium in Magnetars with Applications to Continuous Gravitational Wave Production S. G. Frederick, M.P. Kuchera, Kristen L. Thompson. Monthly Notices of the Royal Astronomical Society, 2021, stab625, https://doi.org/10.1093/mnras/stab625
Bayesian Neural Networks for Fast SUSY Predictions B. Kronheim, M.P. Kuchera, H.B. Prosper, A. Karbo Physics Letters B, Volume 813 (2021) 136041, ISSN 0370-2693, https://doi.org/10.1016/j.physletb.2020.136041
cFAT-GAN: Conditional Simulation of Electron-Proton Scattering Events with Variate Beam Energies by a Feature Augmented and Transformed Generative Adversarial Network L. Velasco, E. McClellan, N. Sato, P. Ambrozewicz, T. Liu, W. Melnitchouk, M.P. Kuchera, Yasir Alanazi, Yaohang Li, 19th IEEE International Conference on Machine Learning and Applications (ICMLA), Miami, FL, USA, 2020, pp. 372-375, doi: 10.1109/ICMLA51294.2020.00066.
A.I. For Nuclear Physics, P. Bedaque, A. Boehnlein, M. Cromaz, M. Diefenthaler, L. Elouadrhiri, T. Horn, M.P. Kuchera, D. Lawrence, D. Lee, S. Lidia, R. McKeown, W. Melnitchouk, W. Nazarewicz, K. Orginos, Y. Roblin, M.S. Smith, M. Schram, Xin-Nian Wang. The European Physical Journal A volume 57, 100 (2021)
Report from the A.I. For Nuclear Physics Workshop, P. Bedaque, A. Boehnlein, M. Cromaz, M. Diefenthaler, L. Elouadrhiri, T. Horn, M.P. Kuchera, D. Lawrence, D. Lee, S. Lidia, R. McKeown, W. Melnitchouk, W. Nazarewicz, K. Orginos, Y. Roblin, M.S. Smith, M. Schram, Xin-Nian Wang. Whitepaper for the Department of Energy and the National Science Foundation. 2020.
Machine Learning Methods for Track Classification in the AT-TPC, M.P. Kuchera, R. Ramanujan, J.Z. Taylor, R.R. Strauss, D. Bazin, J. Bradt, R. Chen. Nucl. Instr. Meth. A. (2019) https://doi.org/10.1016/j.nima.2019.05.097
Study of spectroscopic factors at N= 29 using isobaric analogue resonances in inverse kinematics J. Bradt, Y. Ayyad, D. Bazin, W. Mittig, T. Ahn, S. Beceiro Novo, B.A. Brown, L. Carpenter, M. Cortesi, M.P. Kuchera, W.G. Lynch, S. Rost, N. Watwood, J. Yurkon, J. Barney, U. Datta, J. Estee, A. Gillibert, J. Manfredi, P. Morfouace, D. Pérez-Loureiro, E. Pollacco, J. Sammut, S. Sweany. Phys. Lett. B. (2018) https://doi.org/10.1016/j.physletb.2018.01.015
Commissioning of the Active-Target Time Projection Chamber J. Bradt, D. Bazin, F. Abu-Nimeh, T. Ahn, Y. Ayyad, S. Beceiro-Novo, L. Carpenter, M. Cortesi, M.P. Kuchera, W.G. Lynch, W. Mittig, S. Rost, N. Watwood, J. Yurkon. Nucl. Instr. Meth. A. (2017) https://doi.org/10.1016/j.nima.2017.09.013