NAPAC2022 - List of Authors (Leibman, C.P.)

Paper	Title	Page
TUPA65	Machine Learning for the LANL Electromagnetic Isotope Separator	490
	A. Scheinker, K.W. Dudeck, C.P. Leibman LANL, Los Alamos, New Mexico, USA
	Funding: Los Alamos National Laboratory Electromagnetic Isotope Separator Project. The Los Alamos National Laboratory electromagnetic isotope separator (EMIS) utilizes a Freeman ion source to generate beams of various elements which are accelerated to 40 keV and passed through a 75-degree bend using a large dipole magnet with a radius of 1.2 m. The isotope mass differences translate directly to a spread in momentum, dp, relative to the design momentum p0. Momentum spread is converted to spread in the horizontal arrival location dx at a target chamber by the dispersion of the dipole magnet: dx = D(s)dp/p0. By placing a thin slit leading to a collection chamber at a location xc specific isotope mass is isolated by adjusting the dipole magnet strength or the beam energy. The arriving beam current at xc is associated with average isotope atomic mass, giving an isotope mass spectrum I(m) measured in mA. Although the EMIS is a compact system (5 m) setting up and automatically running at an optimal isotope separation profile I(m) profile is challenging due to time-variation of the complex source as well as un-modeled disturbances. We present preliminary results of developing adaptive machine learning-based tools for the EMIS beam and for the accelerator components.
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA65
About •	Received ※ 18 July 2022 — Revised ※ 07 August 2022 — Accepted ※ 08 August 2022 — Issue date ※ 10 August 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Machine Learning for the LANL Electromagnetic Isotope Separator

490

A. Scheinker, K.W. Dudeck, C.P. Leibman
LANL, Los Alamos, New Mexico, USA

Funding: Los Alamos National Laboratory Electromagnetic Isotope Separator Project.
The Los Alamos National Laboratory electromagnetic isotope separator (EMIS) utilizes a Freeman ion source to generate beams of various elements which are accelerated to 40 keV and passed through a 75-degree bend using a large dipole magnet with a radius of 1.2 m. The isotope mass differences translate directly to a spread in momentum, dp, relative to the design momentum p0. Momentum spread is converted to spread in the horizontal arrival location dx at a target chamber by the dispersion of the dipole magnet: dx = D(s)dp/p0. By placing a thin slit leading to a collection chamber at a location xc specific isotope mass is isolated by adjusting the dipole magnet strength or the beam energy. The arriving beam current at xc is associated with average isotope atomic mass, giving an isotope mass spectrum I(m) measured in mA. Although the EMIS is a compact system (5 m) setting up and automatically running at an optimal isotope separation profile I(m) profile is challenging due to time-variation of the complex source as well as un-modeled disturbances. We present preliminary results of developing adaptive machine learning-based tools for the EMIS beam and for the accelerator components.

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA65

About •

Received ※ 18 July 2022 — Revised ※ 07 August 2022 — Accepted ※ 08 August 2022 — Issue date ※ 10 August 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)