Session

MOCG MC13 Artificial Intelligence and Machine Learning

MOCG
22 Sept 2025, 14:00
Grand Ballroom (Palmer House Hilton Chicago)

Grand Ballroom

Palmer House Hilton Chicago

17 East Monroe Street Chicago, IL 60603, United States of America

Conveners

MOCG MC13 Artificial Intelligence and Machine Learning

  • Mike Fedorov (Lawrence Livermore National Laboratory)
  • Mirjam Lindberg (MAX IV Laboratory)

Presentation materials

There are no materials yet.
Sara Miskovich (SLAC National Accelerator Laboratory)
22/09/2025, 14:00
MC13: Artificial Intelligence & Machine Learning
Contributed Oral Presentation

SLAC and collaborators are developing infrastructure and algorithms for deploying online physics models and combining them with machine learning (ML) models and ML-based feedback from its running accelerators. These models predict details of the beam phase space distribution, include nonlinear collective effects, and leverage high performance computing and ML-based acceleration of simulations...

Diogo Monteiro (European Organization for Nuclear Research)
22/09/2025, 14:15
MC13: Artificial Intelligence & Machine Learning
Contributed 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...

Seongyeol Kim (Pohang Accelerator Laboratory)
22/09/2025, 14:30
MC13: Artificial Intelligence & Machine Learning
Contributed 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...

Sergio Lopez-Caceres (Argonne National Laboratory)
22/09/2025, 14:45
MC13: Artificial Intelligence & Machine Learning
Contributed 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...

Brad Ratto (Los Alamos National Laboratory)
22/09/2025, 15:00
MC13: Artificial Intelligence & Machine Learning
Contributed 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...

Jonathan Edelen (RadiaSoft (United States))
22/09/2025, 15:15
MC13: Artificial Intelligence & Machine Learning
Contributed 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...

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