NAPAC2022 - List of Authors (Mwaniki, M.W.)

Paper	Title	Page
MOPA41	Diagnostics for LINAC Optimization with Machine Learning	139
	R.V. Sharankova, M.W. Mwaniki, K. Seiya, M.E. Wesley Fermilab, Batavia, Illinois, USA
	The Fermilab Linac delivers 400 MeV H⁻ beam to the rest of the accelerator chain. Providing stable intensity, energy, and emittance is key since it directly affects downstream machines. To operate high current beam, accelerators must minimize uncontrolled particle loss; this is generally accomplished by minimizing beam emittance. Ambient temperature and humidity variations are known to affect resonance frequency of the accelerating cavities which induces emittance growth. In addition, the energy and phase space distribution of particles emerging from the ion source are subject to fluctuations. To counter these effects we are working on implementing dynamic longitudinal parameter optimization based on Machine Learning (ML). As an input for the ML model, signals from beam diagnostic have to be well understand and reliable. We have been revisiting diagnostics in the linac. In this presentation we discuss the status of the diagnostics and beam studies as well as the status and plans for ML-based optimization.
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA41
About •	Received ※ 05 August 2022 — Accepted ※ 06 August 2022 — Issue date ※ 07 September 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Diagnostics for LINAC Optimization with Machine Learning

139

R.V. Sharankova, M.W. Mwaniki, K. Seiya, M.E. Wesley
Fermilab, Batavia, Illinois, USA

The Fermilab Linac delivers 400 MeV H⁻ beam to the rest of the accelerator chain. Providing stable intensity, energy, and emittance is key since it directly affects downstream machines. To operate high current beam, accelerators must minimize uncontrolled particle loss; this is generally accomplished by minimizing beam emittance. Ambient temperature and humidity variations are known to affect resonance frequency of the accelerating cavities which induces emittance growth. In addition, the energy and phase space distribution of particles emerging from the ion source are subject to fluctuations. To counter these effects we are working on implementing dynamic longitudinal parameter optimization based on Machine Learning (ML). As an input for the ML model, signals from beam diagnostic have to be well understand and reliable. We have been revisiting diagnostics in the linac. In this presentation we discuss the status of the diagnostics and beam studies as well as the status and plans for ML-based optimization.

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA41

About •

Received ※ 05 August 2022 — Accepted ※ 06 August 2022 — Issue date ※ 07 September 2022

Cite •

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