IPAC2021 - List of Authors (Valentino, G.)

Paper	Title	Page
MOPAB028	Using Machine Learning to Improve Dynamic Aperture Estimates	134
	F.F. Van der Veken, M. Giovannozzi, E.H. Maclean CERN, Geneva, Switzerland C.E. Montanari Bologna University, Bologna, Italy G. Valentino University of Malta, Information and Communication Technology, Msida, Malta
	The dynamic aperture (DA) is an important concept in the study of nonlinear beam dynamics. Several analytical models used to describe the evolution of DA as a function of time, and to extrapolate to realistic time scales that would not be reachable otherwise due to computational limitations, have been successfully developed. Even though these models have been quite successful in the past, the fitting procedure is rather sensitive to several details. Machine Learning (ML) techniques, which have been around for decades and have matured into powerful tools ever since, carry the potential to address some of these challenges. In this paper, two applications of ML approaches are presented and discussed in detail. Firstly, ML has been used to efficiently detect outliers in the DA computations. Secondly, ML techniques have been applied to improve the fitting procedures of the DA models, thus improving their predictive power.
	Poster MOPAB028 [1.764 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB028
About •	paper received ※ 18 May 2021 paper accepted ※ 25 May 2021 issue date ※ 12 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Using Machine Learning to Improve Dynamic Aperture Estimates

134

F.F. Van der Veken, M. Giovannozzi, E.H. Maclean
CERN, Geneva, Switzerland
C.E. Montanari
Bologna University, Bologna, Italy
G. Valentino
University of Malta, Information and Communication Technology, Msida, Malta

The dynamic aperture (DA) is an important concept in the study of nonlinear beam dynamics. Several analytical models used to describe the evolution of DA as a function of time, and to extrapolate to realistic time scales that would not be reachable otherwise due to computational limitations, have been successfully developed. Even though these models have been quite successful in the past, the fitting procedure is rather sensitive to several details. Machine Learning (ML) techniques, which have been around for decades and have matured into powerful tools ever since, carry the potential to address some of these challenges. In this paper, two applications of ML approaches are presented and discussed in detail. Firstly, ML has been used to efficiently detect outliers in the DA computations. Secondly, ML techniques have been applied to improve the fitting procedures of the DA models, thus improving their predictive power.

Poster MOPAB028 [1.764 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB028

About •

paper received ※ 18 May 2021 paper accepted ※ 25 May 2021 issue date ※ 12 August 2021

Export •

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