IPAC2022 - List of Authors (Montanari, C.E.)

Paper	Title	Page
MOPOST042	Using Dynamic Indicators for Probing Single-Particle Stability in Circular Accelerators	168
	C.E. Montanari, A. Bazzani, G. Turchetti Bologna University, Bologna, Italy M. Giovannozzi, C.E. Montanari CERN, Meyrin, Switzerland
	Computing the long-term behaviour of single-particle motion is a numerically intensive process, as it requires a large number of initial conditions to be tracked for a large number of turns to probe their stability. A possibility to reduce the computational resources required is to provide indicators that can efficiently detect chaotic motion, which are considered precursors to unbounded motion. These indicators could allow skilful selection of a set of initial conditions that could then be considered for long-term tracking. The chaotic nature of each orbit can be assessed by using fast-converging dynamic indicators, such as the Fast Lyapunov Indicator (FLI), the Reversibility Error Method (REM), and the Smallest and Global Alignment Index (SALI and GALI). These indicators are widely used in the field of Celestial Mechanics, but not so widespread in Accelerator Physics. They have been applied both to a modulated Hénon map, as a toy model, as well as to realistic lattices of the High-Luminosity LHC. In this paper, we discuss the results of detailed numerical studies, focusing on their performance in detecting chaotic motions.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-MOPOST042
About •	Received ※ 07 June 2022 — Accepted ※ 17 June 2022 — Issue date ※ 02 July 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

MOPOST043	Testing the Global Diffusive Behaviour of Beam-Halo Dynamics at the CERN LHC Using Collimator Scans	172
SUSPMF063	use link to see paper's listing under its alternate paper code
	C.E. Montanari, A. Bazzani Bologna University, Bologna, Italy M. Giovannozzi, C.E. Montanari, S. Redaelli CERN, Meyrin, Switzerland A.A. Gorzawski University of Malta, Information and Communication Technology, Msida, Malta
	In superconducting circular particle accelerators, controlling beam losses is of paramount importance for ensuring optimal machine performance and an efficient operation. To achieve the required level of understanding of the mechanisms underlying beam losses, models based on global diffusion processes have recently been studied and proposed to investigate the beam-halo dynamics. In these models, the building block of the analytical form of the diffusion coefficient is the stability-time estimate of the Nekhoroshev theorem. In this paper, the developed models are applied to data acquired during collimation scans at the CERN LHC. In these measurements, the collimators are moved in steps and the tail population is re-constructed from the observed losses. This allows an estimate of the diffusion coefficient. The results of the analyses performed are presented and discussed in detail.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-MOPOST043
About •	Received ※ 07 June 2022 — Revised ※ 10 June 2022 — Accepted ※ 16 June 2022 — Issue date ※ 17 June 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

MOPOST047	Determination of the Phase-Space Stability Border with Machine Learning Techniques	183
	F.F. Van der Veken, R. Akbari, M.P. Bogaert, E. Fol, M. Giovannozzi, A.L. Lowyck, C.E. Montanari, W. Van Goethem CERN, Meyrin, Switzerland
	The dynamic aperture (DA) of a hadron accelerator is represented by the volume in phase space that exhibits bounded motion, where we disregard any disconnected parts that could be due to stable islands. To estimate DA in numerical simulations, it is customary to sample a set of initial conditions using a polar grid in the transverse planes, featuring a limited number of angles and using evenly distributed radial amplitudes. This method becomes very CPU intensive when detailed scans in 4D, and even more in higher dimensions, are used to compute the dynamic aperture. In this paper, a new method is presented, in which the border of the phase-space stable region is identified using a machine learning (ML) model. This allows one to optimise the computational time by taking the complex geometry of the phase space into account, using adaptive sampling to increase the density of initial conditions along the border of stability.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-MOPOST047
About •	Received ※ 06 June 2022 — Accepted ※ 17 June 2022 — Issue date ※ 20 June 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Using Dynamic Indicators for Probing Single-Particle Stability in Circular Accelerators

168

C.E. Montanari, A. Bazzani, G. Turchetti
Bologna University, Bologna, Italy
M. Giovannozzi, C.E. Montanari
CERN, Meyrin, Switzerland

Computing the long-term behaviour of single-particle motion is a numerically intensive process, as it requires a large number of initial conditions to be tracked for a large number of turns to probe their stability. A possibility to reduce the computational resources required is to provide indicators that can efficiently detect chaotic motion, which are considered precursors to unbounded motion. These indicators could allow skilful selection of a set of initial conditions that could then be considered for long-term tracking. The chaotic nature of each orbit can be assessed by using fast-converging dynamic indicators, such as the Fast Lyapunov Indicator (FLI), the Reversibility Error Method (REM), and the Smallest and Global Alignment Index (SALI and GALI). These indicators are widely used in the field of Celestial Mechanics, but not so widespread in Accelerator Physics. They have been applied both to a modulated Hénon map, as a toy model, as well as to realistic lattices of the High-Luminosity LHC. In this paper, we discuss the results of detailed numerical studies, focusing on their performance in detecting chaotic motions.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-MOPOST042

About •

Received ※ 07 June 2022 — Accepted ※ 17 June 2022 — Issue date ※ 02 July 2022

Cite •

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

MOPOST043

Testing the Global Diffusive Behaviour of Beam-Halo Dynamics at the CERN LHC Using Collimator Scans

172

SUSPMF063

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

C.E. Montanari, A. Bazzani
Bologna University, Bologna, Italy
M. Giovannozzi, C.E. Montanari, S. Redaelli
CERN, Meyrin, Switzerland
A.A. Gorzawski
University of Malta, Information and Communication Technology, Msida, Malta

In superconducting circular particle accelerators, controlling beam losses is of paramount importance for ensuring optimal machine performance and an efficient operation. To achieve the required level of understanding of the mechanisms underlying beam losses, models based on global diffusion processes have recently been studied and proposed to investigate the beam-halo dynamics. In these models, the building block of the analytical form of the diffusion coefficient is the stability-time estimate of the Nekhoroshev theorem. In this paper, the developed models are applied to data acquired during collimation scans at the CERN LHC. In these measurements, the collimators are moved in steps and the tail population is re-constructed from the observed losses. This allows an estimate of the diffusion coefficient. The results of the analyses performed are presented and discussed in detail.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-MOPOST043

About •

Received ※ 07 June 2022 — Revised ※ 10 June 2022 — Accepted ※ 16 June 2022 — Issue date ※ 17 June 2022

Cite •

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

MOPOST047

Determination of the Phase-Space Stability Border with Machine Learning Techniques

183

F.F. Van der Veken, R. Akbari, M.P. Bogaert, E. Fol, M. Giovannozzi, A.L. Lowyck, C.E. Montanari, W. Van Goethem
CERN, Meyrin, Switzerland

The dynamic aperture (DA) of a hadron accelerator is represented by the volume in phase space that exhibits bounded motion, where we disregard any disconnected parts that could be due to stable islands. To estimate DA in numerical simulations, it is customary to sample a set of initial conditions using a polar grid in the transverse planes, featuring a limited number of angles and using evenly distributed radial amplitudes. This method becomes very CPU intensive when detailed scans in 4D, and even more in higher dimensions, are used to compute the dynamic aperture. In this paper, a new method is presented, in which the border of the phase-space stable region is identified using a machine learning (ML) model. This allows one to optimise the computational time by taking the complex geometry of the phase space into account, using adaptive sampling to increase the density of initial conditions along the border of stability.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-MOPOST047

About •

Received ※ 06 June 2022 — Accepted ※ 17 June 2022 — Issue date ※ 20 June 2022

Cite •

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