Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 1863367

Machine Learning/AI for Accelerators : Activities at Fermilab and “vicinity” [Slides]

Shiltsev, Vladimir D.
View original source ↗Read source document ↗Export metadata ↓

Abstract

The presentation addresses 2 issues: Examples and aspirations, and Limits of AI/ML - ''real complexities''.

Explore related subjects

43 PARTICLE ACCELERATORS

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Shiltsev, Vladimir D.. 2022-03-29. Machine Learning/AI for Accelerators : Activities at Fermilab and “vicinity” [Slides]. https://doi.org/10.2172/1863367

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2027

Design and Integration of High Precision Superconducting Magnet Power Supply Systems

This paper reviews the design and integration approach being taken to power more than 400 superconducting magnets in Electron Ion Collider (EIC) by power supplies ranging from 20V to 400V and 100A to 18kA. A major challenge is to integrate existing legacy power supplies with new high current systems and maximize performance and reduce costs. Successful implementation requires coordinated integration of power convertors, current regulation, quench protection, energy extraction, machine protection, controls and existing accelerator infrastructure.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Searching for the Most Harmful Field Errors in the HSR IR Superconducting Magnets

In this project, we improve beam stability for the Electron-Ion Collider. Magnetic field errors can reduce beam stability, making it essential to identify the field errors that have the greatest impact on accelerator performance. However, this is particularly challenging because beam stability depends on the complex interactions of many magnetic field errors, resulting in a high-dimensional and nonlinear optimization problem. We determine which field errors are the most influential for the large physical aperture superconducting magnet B2PF, a critical magnet in the Interaction Region (IR) in the Hadron Storage Ring (HSR). We complete and analyze nearly 30,000 simulations on the Brookhaven National Laboratory Linux Cluster by varying 18 nonlinear magnetic field errors. We evaluate beam stability using the dynamic aperture and the tune diffusion. We identify the field errors that most strongly influence beam stability and establish quantitative field error tolerances that improve accelerator performance.

43 PARTICLE ACCELERATORS↗