255 citations · 497 across the 19 of their papers we have counts for
19 papers
How Architecture and Training Affect TPC Representations Across Experiments
Tyler Wheeler, Michelle P. Kuchera, Raghuram Ramanujan +12
Deep-learning efforts have increasingly shifted toward foundation model approaches. In experimental physics, this allows models and learned representations to be reused beyond the…
Building an AI-native Research Ecosystem for Experimental Particle Physics: A Community Vision
Thea Klaeboe Aarrestad, Alaa Abdelhamid, Haider Abidi +457
Experimental particle physics seeks to understand the universe by probing its fundamental particles and forces and exploring how they govern the large-scale processes that shape co…
Sparse Methods for Vector Embeddings of TPC Data
Tyler Wheeler, Michelle P. Kuchera, Raghuram Ramanujan +7
Time Projection Chambers (TPCs) are versatile detectors that reconstruct charged-particle tracks in an ionizing medium, enabling sensitive measurements across a wide range of nucle…
The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)
Andrew Ferguson, Marisa LaFleur, Lars Ruthotto +97
This community paper developed out of the NSF Workshop on the Future of Artificial Intelligence (AI) and the Mathematical and Physics Sciences (MPS), which was held in March 2025 w…
Classifying metal-poor stars with machine learning using nucleosynthesis calculations
Nicole Vassh, Yilin Wang, Richard M. Woloshyn +4
We apply the capabilities of machine learning (ML) to discern patterns in order to classify metal-poor stars. To do so, we train an ML model on a bank of nucleosynthesis calculatio…
Unpaired Translation of Point Clouds for Modeling Detector Response
Mingyang Li, Michelle Kuchera, Raghuram Ramanujan +3
Modeling detector response is a key challenge in time projection chambers. We cast this problem as an unpaired point cloud translation task, between data collected from simulations…
Implicit Quantile Networks For Emulation in Jet Physics
B. Kronheim, A. Al Kadhim, M. P. Kuchera +2
The ability to model and sample from conditional densities is important in many physics applications. Implicit quantile networks (IQN) have been successfully applied to this task i…
Precision Studies of QCD in the Low Energy Domain of the EIC
V. Burkert, L. Elouadrhiri, A. Afanasev +11
The manuscript focuses on the high impact science of the EIC with objective to identify a portion of the science program for QCD precision studies that requires or greatly benefits…
Two-dimensional total absorption spectroscopy with conditional generative adversarial networks
Cade Dembski, Michelle P. Kuchera, Sean Liddick +2
We explore the use of machine learning techniques to remove the response of large volume -ray detectors from experimental spectra. Segmented -ray total absorption spectromete…
Machine Learning in Nuclear Physics
Amber Boehnlein, Markus Diefenthaler, Cristiano Fanelli +15
Advances in machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of nuclear physics…
Implicit Quantile Neural Networks for Jet Simulation and Correction
Braden Kronheim, Michelle P. Kuchera, Harrison B. Prosper +1
Reliable modeling of conditional densities is important for quantitative scientific fields such as particle physics. In domains outside physics, implicit quantile neural networks (…
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…
Machine Learning Methods for Track Classification in the AT-TPC
Michelle P. Kuchera, Raghuram Ramanujan, Jack Z. Taylor +4
We evaluate machine learning methods for event classification in the Active-Target Time Projection Chamber detector at the National Superconducting Cyclotron Laboratory (NSCL) at M…