Keyword: framework
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TUA2I2 Community Modeling Tools for High Brightness Beam Physics simulation, space-charge, interface, plasma 81
 
  • C.E. Mitchell, M. Garten, A. Huebl, R. Lehé, J. Qiang, R.T. Sandberg, J.-L. Vay
    LBNL, Berkeley, California, USA
 
  Pushing accelerator technology toward operation with higher intensity hadron beams is critical to meet the needs of future colliders, spallation neutron sources, and neutrino sources. To understand the dynamics of such beams requires a community effort with a comprehensive approach to modeling, from the source to the end of the beam lifetime. One needs efficient numerical models with high spatial resolution and particle statistics, insensitivity to numerical noise, and the ability to resolve low-density halo and particle loss. To meet these challenges, LBNL and collaborators have seeded an open ecosystem of codes, the Beam pLasma & Accelerator Simulation Toolkit (BLAST), that can be combined with each other and with machine learning frameworks to enable integrated start-to-end simulation of accelerator beamlines for accelerator design. Examples of BLAST tools include the PIC codes WarpX and ImpactX. These codes feature GPU acceleration and mesh-refinement, and have openPMD standardized data I/O and a Python interface. We describe these tools and the advantages that open community standards provide to inform the modeling and operation of future high-brightness accelerators.  
slides icon Slides TUA2I2 [13.597 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-HB2023-TUA2I2  
About • Received ※ 03 October 2023 — Revised ※ 06 October 2023 — Accepted ※ 09 October 2023 — Issued ※ 01 November 2023
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WEA3C3 Differential Algebra for Accelerator Optimization with Truncated Green’s Function space-charge, simulation, multipole, operation 254
 
  • C.S. Park
    Korea University Sejong Campus, Sejong, Republic of Korea
 
  Accelerator optimization is a critical problem in the design of high-performance particle accelerators. The truncated Green’s function space charge algorithm is a powerful tool for simulating the effects of space charge in accelerators. However, the truncated Green’s function algorithm can be computationally expensive, especially for large accelerators. In this work, we present a new approach to accelerator optimization using differential algebra with the truncated Green’s function space charge algorithm. Our approach uses differential algebra to symbolically represent the equations of the truncated Green’s function algorithm. This allows us to perform efficient symbolic analysis of the equations, which can be used to identify and optimize the accelerator parameters. We demonstrate the effectiveness of our approach by applying it to the optimization of a linear accelerator. We show that our approach can significantly reduce the computational cost of the truncated Green’s function algorithm, while still achieving high accuracy.  
slides icon Slides WEA3C3 [0.772 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-HB2023-WEA3C3  
About • Received ※ 28 September 2023 — Revised ※ 11 October 2023 — Accepted ※ 14 October 2023 — Issued ※ 18 October 2023
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THAFP09 Optimizing Beam Dynamics in LHC with Active Deep Learning network, simulation, dynamic-aperture, hadron 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 icon Slides THAFP09 [1.028 MB]  
poster icon 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
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THBP35 Analysis Tools for Numerical Simulations of Dynamic Aperture with Xsuite simulation, collider, dynamic-aperture, hadron 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 icon 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
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