ALPhA Publications

The future of artificial intelligence and the mathematical and physical sciences (AI+MPS)

Andrew Ferguson, Marisa LaFleur, Lars Ruthotto, Jesse Thaler, Yuan-Sen Ting, Pratyush Tiwary, Soledad Villar

Machine Learning: Science and Technology, 7, 023001, 2026

DOI: https://doi.org/10.1088/2632-2153/ae3e4e

URL: https://iopscience.iop.org/article/10.1088/2632-2153/ae3e4e

Point-cloud based machine learning for classifying rare events in the Active-Target Time Projection Chamber

Poulomi Dey, Adam K Anthony, Curtis Hunt, Michelle P Kuchera, Raghuram Ramanujan, William G Lynch, ManYee Betty Tsang, Joseph M Wieske, Jessica W Ajongbah, Saul Beceiro-Novo, Kyle W Brown, Zbigniew Chajecki, Kaitlin J Cook, Skyler Gangestad, Tom Ginter, Bergen Kendziorski, Fanurs Chi Eh Teh, HoTing Wong*

Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Volume 1072, 2025

DOI: https://doi.org/10.1016/j.nima.2024.170002

URL: https://www.sciencedirect.com/science/article/pii/S0168900224009288

Classifying Metal-poor Stars with Machine Learning Using Nucleosynthesis Calculations

Nicole Vassh, Yilin Wang, Richard M. Woloshyn, Michelle P. Kuchera, Maude Larivière, Kayle Majic, Benoit Côté

The Astrophysical Journal, 992, 36, 2025

DOI: https://doi.org/10.3847/1538-4357/adfc6f

URL: https://iopscience.iop.org/article/10.3847/1538-4357/adfc6f

Sparse Methods for Vector Embeddings of TPC Data

Tyler Wheeler, Michelle P. Kuchera, Raghuram Ramanujan, Ryan Krupp, Chris Wrede, Saiprasad Ravishankar, Connor L. Cross, Hoi Yan Ian Heung, Andrew J. Jones, Benjamin Votaw

NeurIPS Machine Learning and the Physical Sciences Workshop, 2025

DOI: https://doi.org/10.48550/arXiv.2511.11221

URL: https://arxiv.org/abs/2511.11221

Two-dimensional total absorption spectroscopy with conditional generative adversarial networks

C. Dembski, M.P. Kuchera, S. Liddick, R. Ramanujan, A. Spyrou

Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Volume 1060, 2024

DOI: https://doi.org/10.1016/j.nima.2023.169026

URL: https://www.sciencedirect.com/science/article/pii/S0168900223010264?dgcid=coauthor

Implicit quantile networks for emulation in jet physics

B. Kronheim*, A. Al Kadhim, M.P. Kuchera, H.B. Prosper, R. Ramanujan

Machine Learning: Science and Technology, 04507, 2024

DOI: https://doi.org/10.1088/2632-2153/ad9884

URL: https://iopscience.iop.org/article/10.1088/2632-2153/ad9884

Unpaired Translation of Point Clouds for Modeling Detector Response

M. Li, M.P. Kuchera, R. Ramanujan, A. Anthony, C. Hunt, Y. Ayyad

NeurIPS 2024 Machine Learning and the Physical Sciences Workshop, 2024

URL: https://arxiv.org/abs/2501.18674

TensorBNN: Bayesian Inference for Neural Networks using Tensorflow

B. Kronheim, M.P. Kuchera, H.B. Prosper

Computer Physics Communications, 270, 108168, 2022

DOI: https://doi.org/10.1016/j.cpc.2021.108168

URL: https://arxiv.org/abs/2009.14393

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

URL: https://arxiv.org/pdf/2112.02309.pdf

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

Physical Review D, 106, 096002, 2022

URL: https://journals.aps.org/prd/abstract/10.1103/PhysRevD.106.096002

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, 57, 100, 2021

URL: https://link.springer.com/article/10.1140/epja/s10050-020-00290-x

Bayesian Neural Networks for Fast SUSY Predictions

B. Kronheim, M.P. Kuchera, H.B. Prosper, A. Karbo

Physics Letters B, Volume 813, 2021

DOI: https://doi.org/10.1016/j.physletb.2020.136041

URL: https://www.sciencedirect.com/science/article/pii/S0370269320308224

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

DOI: https://doi.org/10.1093/mnras/stab625

URL: https://academic.oup.com/mnras/article-abstract/503/2/2764/6164856

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

DOI: 10.1109/IJCNN52387.2021.9534012

URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9534012&isnumber=9533267

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

DOI: https://doi.org/10.1016/j.nima.2021.165461

URL: https://arxiv.org/abs/2008.02757

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, 2021

DOI: https://doi.org/10.24963/ijcai.2021/293

URL: https://www.ijcai.org/proceedings/2021/293

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

DOI: https://doi.org/10.1051/epjconf/202125103055

URL: https://www.epj-conferences.org/articles/epjconf/pdf/2021/05/epjconf_chep2021_03055.pdf

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

URL: https://arxiv.org/pdf/2111.11415.pdf

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

URL: https://arxiv.org/pdf/2006.05422.pdf

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), 2020

DOI: 10.1109/ICMLA51294.2020.00066

URL: https://ieeexplore.ieee.org/document/9356177

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

Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2019

DOI: https://doi.org/10.1016/j.nima.2019.05.097

URL: https://arxiv.org/pdf/1810.10350.pdf

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

Physics Letters B, 2018

DOI: https://doi.org/10.1016/j.physletb.2018.01.015

URL: https://www.sciencedirect.com/science/article/pii/S0370269318300236

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

Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2017

DOI: https://doi.org/10.1016/j.nima.2017.09.013

URL: https://www.sciencedirect.com/science/article/pii/S0168900217309683