7 papers
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…
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…