IPAC2022 - List of Authors (Edstrom, D.R.)

Paper	Title	Page
MOPOPT058	Machine Learning Training for HOM Reduction in a TESLA-Type Cryomodule at FAST	400
SUSPMF099	use link to see paper's listing under its alternate paper code
	J.A. Diaz Cruz UNM-ECE, Albuquerque, USA J.A. Diaz Cruz, A.L. Edelen, B.T. Jacobson, J.P. Sikora SLAC, Menlo Park, California, USA D.R. Edstrom, A.H. Lumpkin, R.M. Thurman-Keup Fermilab, Batavia, Illinois, USA
	Low emittance electron beams are of high importance at facilities like the Linac Coherent Light Source II (LCLS-II) at SLAC. Emittance dilution effects due to off-axis beam transport for a TESLA-type cryomodule (CM) have been shown at the Fermilab Accelerator Science and Technology (FAST) facility. The results showed the correlation between the electron beam-induced cavity high-order modes (HOMs) and the Beam Position Monitor (BPM) measurements downstream the CM. Mitigation of emittance dilution can be achieved by reducing the HOM signals. Here, we present a couple of Neural Networks (NN) for bunch-by-bunch mean prediction and standard deviation prediction for BPMs located downstream the CM.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-MOPOPT058
About •	Received ※ 15 June 2022 — Revised ※ 18 June 2022 — Accepted ※ 24 June 2022 — Issue date ※ 26 June 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

MOPOPT058

Machine Learning Training for HOM Reduction in a TESLA-Type Cryomodule at FAST

400

SUSPMF099

use link to see paper's listing under its alternate paper code

J.A. Diaz Cruz
UNM-ECE, Albuquerque, USA
J.A. Diaz Cruz, A.L. Edelen, B.T. Jacobson, J.P. Sikora
SLAC, Menlo Park, California, USA
D.R. Edstrom, A.H. Lumpkin, R.M. Thurman-Keup
Fermilab, Batavia, Illinois, USA

Low emittance electron beams are of high importance at facilities like the Linac Coherent Light Source II (LCLS-II) at SLAC. Emittance dilution effects due to off-axis beam transport for a TESLA-type cryomodule (CM) have been shown at the Fermilab Accelerator Science and Technology (FAST) facility. The results showed the correlation between the electron beam-induced cavity high-order modes (HOMs) and the Beam Position Monitor (BPM) measurements downstream the CM. Mitigation of emittance dilution can be achieved by reducing the HOM signals. Here, we present a couple of Neural Networks (NN) for bunch-by-bunch mean prediction and standard deviation prediction for BPMs located downstream the CM.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-MOPOPT058

About •

Received ※ 15 June 2022 — Revised ※ 18 June 2022 — Accepted ※ 24 June 2022 — Issue date ※ 26 June 2022

Cite •

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