Conveners
MOCG MC13 Artificial Intelligence and Machine Learning
- Mike Fedorov (Lawrence Livermore National Laboratory)
- Mirjam Lindberg (MAX IV Laboratory)
-
Sara Miskovich (SLAC National Accelerator Laboratory)22/09/2025, 14:00MC13: Artificial Intelligence & Machine LearningContributed Oral Presentation
SLAC and collaborators are developing infrastructure and algorithms for deploying online physics models, combining them with online machine learning (ML) models, and using both of these in tandem for ML-based optimization and control of accelerators. These system models can predict details of the beam phase space distribution, include nonlinear collective effects, and leverage high...
Go to contribution page -
Diogo Monteiro (European Organization for Nuclear Research)22/09/2025, 14:15MC13: Artificial Intelligence & Machine LearningContributed Oral Presentation
In times of concern over the environmental impact of high-energy physics organizations, our research in CERN's Cooling and Ventilation group in the Engineering department investigates energy-saving strategies for heating, ventilation, and air conditioning (HVAC) systems. Widely used in both residential and industrial settings, HVAC systems contribute up to 40% of residential and 70% of...
Go to contribution page -
Jonathan Edelen (RadiaSoft (United States))22/09/2025, 14:30MC13: Artificial Intelligence & Machine LearningContributed Oral Presentation
Neutron scattering experiments are a critical tool for the exploration of molecular structure in compounds. The TOPAZ single crystal diffractometer at the Spallation Neutron Source and the Powder Diffractometer at the High Flux Isotope Reactor study these samples by illuminating them with different energy neutron beams and recording the scattered neutrons. Aligning and maintaining the...
Go to contribution page -
Seongyeol Kim (Pohang Accelerator Laboratory)22/09/2025, 14:45MC13: Artificial Intelligence & Machine LearningContributed Oral Presentation
Optimization of the transfer line against collective effects such as space charge and coherent synchrotron radiation (CSR) effects is crucial to preserve the beam quality. While simple conventional diagnostic methods provide ensemble averaged beam parameters or limited information of phase space, they are still limited in obtaining precise, complete 6-dimensional phase space with all the...
Go to contribution page -
Sergio Lopez-Caceres (Argonne National Laboratory)22/09/2025, 15:00MC13: Artificial Intelligence & Machine LearningContributed Oral Presentation
The Californium Rare Isotope Breeder Upgrade (CARIBU) at Argonne National Laboratory is a pivotal facility for studying rare and unstable atomic nuclei, providing radioactive ion beams (RIBs) from the spontaneous fission of Californium-252. Since 2008, CARIBU has significantly impacted nuclear structure studies, nuclear astrophysics research, and national security applications. However, the...
Go to contribution page -
Brad Ratto (Los Alamos National Laboratory)22/09/2025, 15:15MC13: Artificial Intelligence & Machine LearningContributed Oral Presentation
Machine learning methods have been increasingly used to model complex physical processes that are difficult to address with traditional approaches, especially when these processes exhibit temporal dynamics or require real-time implementation. The linear accelerator (LINAC) at the LANSCE facility is one such system. While a high-resolution simulation tool, HPSim, exists, the complexity and high...
Go to contribution page