LINAC2022 - List of Keywords (software)

Paper	Title	Other Keywords	Page
MOPOGE05	Effect of High-Magnetic Field Region Geometry on the Efficiency of a 750 MHz IH Structure	cavity, linac, simulation, impedance	150
	G. Moreno, P. Calvo, D. Gavela, J. Giner Navarro, R. López López, C. Oliver, J.M. Pérez Morales CIEMAT, Madrid, Spain M.C. Battaglia, J.M. Carmona AVS, Elgoibar, Spain A.M. Lombardi CERN, Meyrin, Switzerland
	Funding: CIEMAT High frequency structures generally translate to high efficiency performances thanks to reduced surfaces of the inner cavity. Two round-profiles geometry and some variations of two important parameters of a 750 MHz IH-DTL are proposed in this paper in order to improve shunt impedance performance regarding an existing solution with flat-walled cavity developed by CERN. The proposed designs are shaped such that they guarantee an easy connection of RF and vacuum auxiliaries. Electromagnetic simulations are checked with CST Microwave Studio.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-LINAC2022-MOPOGE05
About •	Received ※ 20 August 2022 — Revised ※ 22 August 2022 — Accepted ※ 27 August 2022 — Issue date ※ 13 October 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THPOPA26	Machine Learning Assisted Cavity Quench Identification at the European XFEL	cavity, FEL, operation, hardware	798
	J. Branlard, A. Eichler, J.H.K. Timm, N. Walker DESY, Hamburg, Germany
	A server-based quench detection system is used since the beginning of operation at the European XFEL (2017) to stop driving superconducting cavities if they experience a quench. While this approach effectively detects quenches, it also generates false positives, tripping the accelerating stations when failures other than quenches occur. Using the post-mortem data snapshots generated for every trip, an additional signal (referred to as residual) is systematically computed based on the standard cavity model. Following an initial training on a set of such residuals derived from quench as well as non-quench events, two independent machine learning engines analyze routinely the trip snapshots and their residuals to identify if a trip was indeed triggered by a quench or has another root cause. The outcome of the analysis is automatically appended to the data snapshots and distributed to a team of experts. This constitutes a fully deployed example of machine-learning-assisted failure classification to identify quenches, supporting experts in their daily routine of monitoring and documenting the accelerator uptime and availability.
	Slides THPOPA26 [0.695 MB]
	Poster THPOPA26 [0.975 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-LINAC2022-THPOPA26
About •	Received ※ 19 August 2022 — Revised ※ 24 August 2022 — Accepted ※ 01 September 2022 — Issue date ※ 01 September 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Other Keywords

Page

MOPOGE05

Effect of High-Magnetic Field Region Geometry on the Efficiency of a 750 MHz IH Structure

cavity, linac, simulation, impedance

150

G. Moreno, P. Calvo, D. Gavela, J. Giner Navarro, R. López López, C. Oliver, J.M. Pérez Morales
CIEMAT, Madrid, Spain
M.C. Battaglia, J.M. Carmona
AVS, Elgoibar, Spain
A.M. Lombardi
CERN, Meyrin, Switzerland

Funding: CIEMAT
High frequency structures generally translate to high efficiency performances thanks to reduced surfaces of the inner cavity. Two round-profiles geometry and some variations of two important parameters of a 750 MHz IH-DTL are proposed in this paper in order to improve shunt impedance performance regarding an existing solution with flat-walled cavity developed by CERN. The proposed designs are shaped such that they guarantee an easy connection of RF and vacuum auxiliaries. Electromagnetic simulations are checked with CST Microwave Studio.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-LINAC2022-MOPOGE05

About •

Received ※ 20 August 2022 — Revised ※ 22 August 2022 — Accepted ※ 27 August 2022 — Issue date ※ 13 October 2022

Cite •

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

THPOPA26

Machine Learning Assisted Cavity Quench Identification at the European XFEL

cavity, FEL, operation, hardware

798

J. Branlard, A. Eichler, J.H.K. Timm, N. Walker
DESY, Hamburg, Germany

A server-based quench detection system is used since the beginning of operation at the European XFEL (2017) to stop driving superconducting cavities if they experience a quench. While this approach effectively detects quenches, it also generates false positives, tripping the accelerating stations when failures other than quenches occur. Using the post-mortem data snapshots generated for every trip, an additional signal (referred to as residual) is systematically computed based on the standard cavity model. Following an initial training on a set of such residuals derived from quench as well as non-quench events, two independent machine learning engines analyze routinely the trip snapshots and their residuals to identify if a trip was indeed triggered by a quench or has another root cause. The outcome of the analysis is automatically appended to the data snapshots and distributed to a team of experts. This constitutes a fully deployed example of machine-learning-assisted failure classification to identify quenches, supporting experts in their daily routine of monitoring and documenting the accelerator uptime and availability.

Slides THPOPA26 [0.695 MB]

Poster THPOPA26 [0.975 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-LINAC2022-THPOPA26

About •

Received ※ 19 August 2022 — Revised ※ 24 August 2022 — Accepted ※ 01 September 2022 — Issue date ※ 01 September 2022

Cite •

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