255 citations · 405 across the 33 of their papers we have counts for
36 papers
Reinforcement Learning Techniques for the Optimization of Target Polarization in Nuclear Physics Scattering Experiments
Armen Kasparian, Torri Jeske, Monibor Rahman +5
The operation of dynamically polarized targets in nuclear physics experiments relies on continuous tuning of the microwave frequency to compensate for radiation damage and evolving…
SMOCS: A Streaming Framework for Simplified Deployment, Monitoring, and Optimization of ML Systems in Production
Armen Kasparian, Kishansingh Rajput, Malachi Schram +1
Machine learning has demonstrated significant potential for real-time monitoring, optimization, and control of scientific facilities. However, deploying and maintaining ML models i…
Uncertainty Guided Online Ensemble for Non-stationary Data Streams in Fusion Science
Kishansingh Rajput, Malachi Schram, Brian Sammuli +1
Machine Learning (ML) is poised to play a pivotal role in the development and operation of next-generation fusion devices. Fusion data shows non-stationary behavior with distributi…
Toward an event-level analysis of hadron structure using differential programming
Kevin Braga, Markus Diefenthaler, Steven Goldenberg +7
Reconstructing the internal properties of hadrons in terms of fundamental quark and gluon degrees of freedom is a central goal in nuclear and particle physics. This effort lies at…
Geometric GNNs for Charged Particle Tracking at GlueX
Ahmed Hossam Mohammed, Kishansingh Rajput, Simon Taylor +3
Nuclear physics experiments are aimed at uncovering the fundamental building blocks of matter. The experiments involve high-energy collisions that produce complex events with many…
Outlook Towards Deployable Continual Learning for Particle Accelerators
Kishansingh Rajput, Sen Lin, Auralee Edelen +2
Particle Accelerators are high power complex machines. To ensure uninterrupted operation of these machines, thousands of pieces of equipment need to be synchronized, which requires…