6 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…
eLog analysis for accelerators: status and future outlook
Antonin Sulc, Thorsten Hellert, Aaron Reed +11
This work demonstrates electronic logbook (eLog) systems leveraging modern AI-driven information retrieval capabilities at the accelerator facilities of Fermilab, Jefferson Lab, La…
ML-based Calibration and Control of the GlueX Central Drift Chamber
Thomas Britton, Michael Goodrich, Naomi Jarvis +5
The GlueX Central Drift Chamber (CDC) in Hall D at Jefferson Lab, used for detecting and tracking charged particles, is calibrated and controlled during data taking using a Gaussia…
Hydra: Computer Vision for Data Quality Monitoring
Thomas Britton, Torri Jeske, David Lawrence +1
Hydra is a system which utilizes computer vision to perform near real time data quality management, initially developed for Hall-D in 2019. Since then, it has been deployed across…
AI Assisted Experiment Control and Calibration
Thomas Britton, Michael Goodrich, Naomi Jarvis +4
Final report for the AI Assisted Experiment Control and Calibration project. This project integrated AI/ML into the controls and calibration of a production detector system in the…
Distance Preserving Machine Learning for Uncertainty Aware Accelerator Capacitance Predictions
Steven Goldenberg, Malachi Schram, Kishansingh Rajput +7
Providing accurate uncertainty estimations is essential for producing reliable machine learning models, especially in safety-critical applications such as accelerator systems. Gaus…