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proceedings of accelerator conferences held around the world
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| URL | https://doi.org/10.18429/JACoW-IPAC2026-MOP6311 |
|---|---|
| Title | AI-ready lattice representation and ML optimization for the BNL booster-to-AGS transfer line |
| Authors |
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| Abstract | 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. We establish an AI-ready representation of the BtA lattice using the Particle Accelerator Language Standard (PALS), extended with semantic metadata linking lattice elements to control system signals and device capabilities. This NARAD-PALS model enables direct mapping between simulation, operational devices, and machine data. We implement this framework for the BtA line and demonstrate semantic device queries and control-channel resolution within the BNL Accelerator Device Objects (ADO) system. This unified representation supports integration of streaming and archived data and provides a foundation for ML-based optimization of AGS injection and cross-facility interoperability. |
| Paper | download: MOP6311.pdf |
| Cite | BibTeX, LaTeX, Text/Word, RIS, EndNote |
| Conference | 17th International Particle Accelerator Conference |
| Series | |
| Location | Deauville, France |
| Date | 17-22 May 2026 |
| Publisher | JACoW Publishing, Geneva, Switzerland |
| Editorial Board | Editorial Board |
| Online ISBN | 978-3-95450-252-3 |
| Online ISSN | 2673-5350 |
| Received | 12 May 2026 |
| Revised | 15 May 2026 |
| Accepted | 16 May 2026 |
| Issued | 20 July 2026 |
| DOI | 10.18429/JACoW-IPAC2026-MOP6311 |
| Pages | 407-410 |
| Copyright | Published by JACoW Publishing under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI. |