IPAC2021 - List of Authors (Van der Veken, F.F.)

Paper	Title	Page
MOPAB027	Improving the Luminosity Burn-Off Estimate by Considering Single-Diffractive Effects	130
	F.F. Van der Veken, H. Burkhardt, M. Giovannozzi, V.K.B. Olsen CERN, Geneva, Switzerland
	Collisions in a high-luminosity collider result in a continuous burn-off of the circulating beams that is the dominant effect that reduces the instantaneous luminosity over time. In order to obtain a good estimate of the luminosity evolution, it is imperative to have an accurate understanding of the burn-off. Typically, this is calculated based on the inelastic cross-section, as it provides a direct estimate of the number of protons that participate in inelastic collisions, and are hence removed. Likewise, protons that participate in elastic collisions will remain in the machine acceptance, still contributing to luminosity. In between these two regimes lie diffractive collisions, for which the protons have a certain probability to remain in the machine acceptance. Recent developments of the SixTrack code allow it to interface with Pythia, thus allowing for more precise simulations to obtain a better estimate of the diffractive part of the cross-section. In this paper, we will mainly concentrate on slowly-drifting protons that are close to the acceptance limit, resulting from single-diffractive scattering.
	Poster MOPAB027 [1.193 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB027
About •	paper received ※ 18 May 2021 paper accepted ※ 31 May 2021 issue date ※ 11 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

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)

WEPAB026	Optics Measurements and Correction Plans for the HL-LHC	2656
	T.H.B. Persson, X. Buffat, F.S. Carlier, R. De Maria, J. Dilly, E. Fol, D. Gamba, H. Garcia Morales, A. García-Tabarés Valdivieso, M. Giovannozzi, M. Hofer, E.J. Høydalsvik, J. Keintzel, M. Le Garrec, E.H. Maclean, L. Malina, P.K. Skowroński, F. Soubelet, R. Tomás García, F.F. Van der Veken, A. Wegscheider, D.W. Wolf, L. van Riesen-Haupt CERN, Meyrin, Switzerland J.M. Coello de Portugal PSI, Villigen PSI, Switzerland
	The High Luminosity LHC (HL-LHC) will require stringent optics correction to operate safely and deliver the design luminosity to the experiments. In order to achieve this, several new methods for optics correction have been developed. In this article, we outline some of these methods and we describe the envisioned strategy of how to use them in order to reach the challenging requirements of the HL-LHC physics program.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB026
About •	paper received ※ 17 May 2021 paper accepted ※ 27 July 2021 issue date ※ 30 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Improving the Luminosity Burn-Off Estimate by Considering Single-Diffractive Effects

130

F.F. Van der Veken, H. Burkhardt, M. Giovannozzi, V.K.B. Olsen
CERN, Geneva, Switzerland

Collisions in a high-luminosity collider result in a continuous burn-off of the circulating beams that is the dominant effect that reduces the instantaneous luminosity over time. In order to obtain a good estimate of the luminosity evolution, it is imperative to have an accurate understanding of the burn-off. Typically, this is calculated based on the inelastic cross-section, as it provides a direct estimate of the number of protons that participate in inelastic collisions, and are hence removed. Likewise, protons that participate in elastic collisions will remain in the machine acceptance, still contributing to luminosity. In between these two regimes lie diffractive collisions, for which the protons have a certain probability to remain in the machine acceptance. Recent developments of the SixTrack code allow it to interface with Pythia, thus allowing for more precise simulations to obtain a better estimate of the diffractive part of the cross-section. In this paper, we will mainly concentrate on slowly-drifting protons that are close to the acceptance limit, resulting from single-diffractive scattering.

Poster MOPAB027 [1.193 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB027

About •

paper received ※ 18 May 2021 paper accepted ※ 31 May 2021 issue date ※ 11 August 2021

Export •

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

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)

WEPAB026

Optics Measurements and Correction Plans for the HL-LHC

2656

T.H.B. Persson, X. Buffat, F.S. Carlier, R. De Maria, J. Dilly, E. Fol, D. Gamba, H. Garcia Morales, A. García-Tabarés Valdivieso, M. Giovannozzi, M. Hofer, E.J. Høydalsvik, J. Keintzel, M. Le Garrec, E.H. Maclean, L. Malina, P.K. Skowroński, F. Soubelet, R. Tomás García, F.F. Van der Veken, A. Wegscheider, D.W. Wolf, L. van Riesen-Haupt
CERN, Meyrin, Switzerland
J.M. Coello de Portugal
PSI, Villigen PSI, Switzerland

The High Luminosity LHC (HL-LHC) will require stringent optics correction to operate safely and deliver the design luminosity to the experiments. In order to achieve this, several new methods for optics correction have been developed. In this article, we outline some of these methods and we describe the envisioned strategy of how to use them in order to reach the challenging requirements of the HL-LHC physics program.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB026

About •

paper received ※ 17 May 2021 paper accepted ※ 27 July 2021 issue date ※ 30 August 2021

Export •

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