Speaker
Description
As part of the Nuclear Physics AI-Ready Accelerator Data (NARAD) project, Brookhaven National Laboratory is developing a demonstration use case based on the Booster-to-AGS (BtA) transfer line. The effort aims to establish a standardized, AI-ready representation of the BtA lattice using the Particle Accelerator Lattice Standard (PALS), which enables seamless conversion across simulation programs and aligns simulation models with the underlying ontology of the physical accelerator and its data systems. This ontology-driven framework provides a common semantic layer that supports machine learning workflows for accelerator modeling and optimization. With an AI-ready BtA model, we will explore ML-based optimization of AGS injection quality in simulation. This work demonstrates how standardized data structures and shared vocabularies can accelerate AI applications in accelerator facilities and serve as a prototype for cross-facility interoperability within the NARAD ecosystem.
Funding Agency
Work supported by Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy and by DOE-NP No. DE-SC0024287.
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