HB2023 - List of Authors (Di Croce, D.)

Paper	Title	Page
TUA2I1	Xsuite: An Integrated Beam Physics Simulation Framework	73
	G. Iadarola, A. Abramov, X. Buffat, R. De Maria, D. Demetriadou, L. Deniau, P.D. Hermes, P. Kicsiny, P.M. Kruyt, A. Latina, S. Łopaciuk, L. Mether, K. Paraschou, T. Pieloni, G. Sterbini, F.F. Van der Veken CERN, Meyrin, Switzerland P. Belanger UBC & TRIUMF, Vancouver, British Columbia, Canada D. Di Croce, M. Seidel, L. van Riesen-Haupt EPFL, Lausanne, Switzerland P.J. Niedermayer GSI, Darmstadt, Germany
	Xsuite is a newly developed modular simulation package combining in a single flexible and modern framework the capabilities of different tools developed at CERN in the past decades, notably Sixtrack, Sixtracklib, COMBI and PyHEADTAIL. The suite is made of a set of python modules (Xobjects, Xparts, Xtrack, Xcoll, Xfields, Xdpes) that can be flexibly combined together and with other accelerator-specific and general-purpose python tools to study complex simulation scenarios. The code allows for symplectic modeling of the particle dynamics, combined with the effect of synchrotron radiation, impedances, feedbacks, space charge, electron cloud, beam-beam, beamstrahlung, electron lenses. For collimation studies, beam-matter interaction is simulated using the K2 scattering model or interfacing Xsuite with the BDSIM/Geant4 library. Tools are available to compute the accelerator optics functions from the tracking model and to generate particle distributions matched to the optics. Different computing platforms are supported, including conventional CPUs, as well as GPUs from different vendors.
	Slides TUA2I1 [4.388 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-HB2023-TUA2I1
About •	Received ※ 30 September 2023 — Revised ※ 07 October 2023 — Accepted ※ 10 October 2023 — Issued ※ 22 October 2023
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THAFP09	Optimizing Beam Dynamics in LHC with Active Deep Learning	422
	D. Di Croce, T. Pieloni, M. Seidel EPFL, Lausanne, Switzerland M. Giovannozzi, F.F. Van der Veken CERN, Meyrin, Switzerland E. Krymova SDSC, Lausanne, Switzerland M. Seidel PSI, Villigen PSI, Switzerland
	The Dynamic Aperture (DA) is an important concept for the study of non-linear beam dynamics in a circular accelerator. It refers to the region in phase space where a particle’s motion remains bounded over a given number of turns. Understanding the features of DA is crucial for operating circular accelerators as it provides insights on non-linear beam dynamics and the phenomena affecting beam lifetime. The standard approach to calculate the DA is computationally very intensive. In our study, we aim at determining an optimal set of parameters that affect DA, like betatron tune, chromaticity, and Landau octupole strengths, using a Deep Neural Network (DNN) model. The DNN model predicts the so-called angular DA, based on simulated LHC data. To enhance its performance, we integrated the DNN model into an innovative Active Learning (AL) framework. This framework not only enables retraining and updating of the model, but also facilitates efficient data generation through smart sampling. The results demonstrate that the use of the Active Learning (AL) framework allows faster scanning of LHC ring configuration parameters without compromising the accuracy of the DA calculations.
	Slides THAFP09 [1.028 MB]
	Poster THAFP09 [6.173 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-HB2023-THAFP09
About •	Received ※ 01 October 2023 — Revised ※ 04 October 2023 — Accepted ※ 10 October 2023 — Issued ※ 31 October 2023
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THBP35	Analysis Tools for Numerical Simulations of Dynamic Aperture with Xsuite	551
	T. Pugnat, M. Giovannozzi, F.F. Van der Veken CERN, Meyrin, Switzerland D. Di Croce EPFL, Lausanne, Switzerland
	Recently, several efforts have been made at CERN to develop a new tracking tool, Xsuite, which is intended to be the successor to SixTrack. In this framework, analysis tools have also been prepared with the goal of providing advanced post-processing techniques for the interpretation of dynamic aperture simulations. The proposed software suite, named Xdyna, is meant to be a successor to the existing SixDesk environment. It incorporates all recent approaches developed to determine the dynamic aperture for a fixed number of turns. It also enables studying the time evolution of the dynamic aperture and the fitting of rigorous models based on the stability-time estimate provided by the Nekhoroshev theorem. These models make it possible to link the dynamic aperture to beam lifetime, and thus provide very relevant information for the actual performance of particle colliders. These tools have been applied to studies related to the luminosity upgrade of the CERN Large Hadron Collider (HL-LHC), the results of which are presented here.
	Poster THBP35 [0.514 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-HB2023-THBP35
About •	Received ※ 28 September 2023 — Revised ※ 05 October 2023 — Accepted ※ 10 October 2023 — Issued ※ 11 October 2023
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Xsuite: An Integrated Beam Physics Simulation Framework

73

G. Iadarola, A. Abramov, X. Buffat, R. De Maria, D. Demetriadou, L. Deniau, P.D. Hermes, P. Kicsiny, P.M. Kruyt, A. Latina, S. Łopaciuk, L. Mether, K. Paraschou, T. Pieloni, G. Sterbini, F.F. Van der Veken
CERN, Meyrin, Switzerland
P. Belanger
UBC & TRIUMF, Vancouver, British Columbia, Canada
D. Di Croce, M. Seidel, L. van Riesen-Haupt
EPFL, Lausanne, Switzerland
P.J. Niedermayer
GSI, Darmstadt, Germany

Xsuite is a newly developed modular simulation package combining in a single flexible and modern framework the capabilities of different tools developed at CERN in the past decades, notably Sixtrack, Sixtracklib, COMBI and PyHEADTAIL. The suite is made of a set of python modules (Xobjects, Xparts, Xtrack, Xcoll, Xfields, Xdpes) that can be flexibly combined together and with other accelerator-specific and general-purpose python tools to study complex simulation scenarios. The code allows for symplectic modeling of the particle dynamics, combined with the effect of synchrotron radiation, impedances, feedbacks, space charge, electron cloud, beam-beam, beamstrahlung, electron lenses. For collimation studies, beam-matter interaction is simulated using the K2 scattering model or interfacing Xsuite with the BDSIM/Geant4 library. Tools are available to compute the accelerator optics functions from the tracking model and to generate particle distributions matched to the optics. Different computing platforms are supported, including conventional CPUs, as well as GPUs from different vendors.

Slides TUA2I1 [4.388 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-HB2023-TUA2I1

About •

Received ※ 30 September 2023 — Revised ※ 07 October 2023 — Accepted ※ 10 October 2023 — Issued ※ 22 October 2023

Cite •

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

THAFP09

Optimizing Beam Dynamics in LHC with Active Deep Learning

422

D. Di Croce, T. Pieloni, M. Seidel
EPFL, Lausanne, Switzerland
M. Giovannozzi, F.F. Van der Veken
CERN, Meyrin, Switzerland
E. Krymova
SDSC, Lausanne, Switzerland
M. Seidel
PSI, Villigen PSI, Switzerland

The Dynamic Aperture (DA) is an important concept for the study of non-linear beam dynamics in a circular accelerator. It refers to the region in phase space where a particle’s motion remains bounded over a given number of turns. Understanding the features of DA is crucial for operating circular accelerators as it provides insights on non-linear beam dynamics and the phenomena affecting beam lifetime. The standard approach to calculate the DA is computationally very intensive. In our study, we aim at determining an optimal set of parameters that affect DA, like betatron tune, chromaticity, and Landau octupole strengths, using a Deep Neural Network (DNN) model. The DNN model predicts the so-called angular DA, based on simulated LHC data. To enhance its performance, we integrated the DNN model into an innovative Active Learning (AL) framework. This framework not only enables retraining and updating of the model, but also facilitates efficient data generation through smart sampling. The results demonstrate that the use of the Active Learning (AL) framework allows faster scanning of LHC ring configuration parameters without compromising the accuracy of the DA calculations.

Slides THAFP09 [1.028 MB]

Poster THAFP09 [6.173 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-HB2023-THAFP09

About •

Received ※ 01 October 2023 — Revised ※ 04 October 2023 — Accepted ※ 10 October 2023 — Issued ※ 31 October 2023

Cite •

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

THBP35

Analysis Tools for Numerical Simulations of Dynamic Aperture with Xsuite

551

T. Pugnat, M. Giovannozzi, F.F. Van der Veken
CERN, Meyrin, Switzerland
D. Di Croce
EPFL, Lausanne, Switzerland

Recently, several efforts have been made at CERN to develop a new tracking tool, Xsuite, which is intended to be the successor to SixTrack. In this framework, analysis tools have also been prepared with the goal of providing advanced post-processing techniques for the interpretation of dynamic aperture simulations. The proposed software suite, named Xdyna, is meant to be a successor to the existing SixDesk environment. It incorporates all recent approaches developed to determine the dynamic aperture for a fixed number of turns. It also enables studying the time evolution of the dynamic aperture and the fitting of rigorous models based on the stability-time estimate provided by the Nekhoroshev theorem. These models make it possible to link the dynamic aperture to beam lifetime, and thus provide very relevant information for the actual performance of particle colliders. These tools have been applied to studies related to the luminosity upgrade of the CERN Large Hadron Collider (HL-LHC), the results of which are presented here.

Poster THBP35 [0.514 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-HB2023-THBP35

About •

Received ※ 28 September 2023 — Revised ※ 05 October 2023 — Accepted ※ 10 October 2023 — Issued ※ 11 October 2023

Cite •

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