4 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…
Explainable physics-based constraints on reinforcement learning for accelerator controls
Jonathan Colen, Malachi Schram, Kishansingh Rajput +1
We present a reinforcement learning (RL) framework for controlling particle accelerator experiments that builds explainable physics-based constraints on agent behavior. The goal is…
Harnessing the Power of Gradient-Based Simulations for Multi-Objective Optimization in Particle Accelerators
Kishansingh Rajput, Malachi Schram, Auralee Edelen +6
Particle accelerator operation requires simultaneous optimization of multiple objectives. Multi-Objective Optimization (MOO) is particularly challenging due to trade-offs between t…