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Eito Iwai (Japan Synchrotron Radiation Research Institute, RIKEN SPring-8 Center)19/05/2026, 12:00MC6.D13: Instrumentation: Artificial IntelligenceInvited Oral Presentation
Leveraging Machine Learning (ML), we aim to automate and simplify the complex tuning of the XFEL light source accelerator, SACLA, thereby delivering extreme XFEL performance tailored to experimental user needs. Since 2020, we have implemented a Bayesian Optimization (BO)-based automated tuning framework at SACLA. This enables us to meet the detailed XFEL requirements for the approximately ten...
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Ysabella Kassandra Ong (Istituto Nazionale di Fisica Nucleare)20/05/2026, 15:40MC6.D13: Instrumentation: Artificial IntelligenceContributed Oral Presentation
The application of Artificial Intelligence (AI) and Machine Learning (ML) in particle accelerator systems has become an effective strategy for handling complex operations and enhancing performance. At INFN-Legnaro National Laboratories (INFN-LNL), both offline and online AI/ML-driven approaches have been developed to improve beam dynamics, reduce setup times, and increase overall accelerator...
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Laura Torino (ALBA Synchrotron (Spain))21/05/2026, 11:00MC6.T03: Beam Diagnostics and InstrumentationInvited Oral Presentation
The development of Beam Position Monitors (BPMs), and particularly the design of the button pickup, is critically important with the new generation of Synchrotron Light Sources. Specifically, the miniaturization of the vacuum pipe and the broadening of the beam spectrum present special challenges for the button's design to meet the superior stability requirements demanded by new feedback...
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Sarah GEFFROY (Université Paris-Saclay, CNRS/IN2P3, IJCLab)21/05/2026, 11:30MC6.T03: Beam Diagnostics and InstrumentationContributed Oral Presentation
On behalf of the GBAR collaboration
The GBAR (Gravitational Behavior of Antihydrogen at Rest) experiment receives particle beams from the AD/ELENA facility at CERN, the world's unique source of low-energy antiprotons.The AD/ELENA facility is also unique in its deceleration of GeV beams created using CERN proton-synchroton (PS) machine. * According to the 2023 review, the beam delivered...
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Mayu Wada (The University of Tokyo)21/05/2026, 11:50MC6.T03: Beam Diagnostics and InstrumentationContributed Oral Presentation
The E34 experiment at J-PARC MLF aims to precisely measure the positive muon's anomalous magnetic moment and electric dipole moment.
Two technical challenges are critical. First, the ultraslow muon source (from muon cooling) must achieve its target intensity ($10^6 \mu^+/\text{sec}$) and low-emittance ($\epsilon_{x, \text{rms,normalized}}: \sim 0.3 \pi [\text{mm}\cdot\text{mrad}],...
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Renjun Yang (Institute of High Energy Physics)21/05/2026, 12:10MC6.T03: Beam Diagnostics and InstrumentationContributed Oral Presentation
Achieving sustainable beam operation in high-power accelerators requires careful control and minimization of halo-particle-induced beam loss. To accomplish this, it is important to have a clear understanding of the halo-particle distribution. While state-of-the-art instruments can achieve a dynamic range of ~10^6 with counting readout schemes, a novel fluorescence wire scanner combined with a...
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Frank Ludwig (Deutsches Elektronen-Synchrotron DESY)22/05/2026, 09:00MC6.T27: Instrumentation: Low Level RFInvited Oral Presentation
In the past two decades, RF controls have improved by two orders in
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magnitude achieving meanwhile sub-10 fs phase stabilities and 10e-4
amplitude precision. Analog-to-digital-converters (ADCs) are the main
limitation for further increase in detector resolution. Alternative
architectures are therefore needed to overcome this limitation. The
presented work covers a novel application of the... -
Thorsten Hellert (Lawrence Berkeley National Laboratory)22/05/2026, 09:30MC6.D13: Instrumentation: Artificial IntelligenceContributed Oral Presentation
As agentic AI systems enter accelerator operations, a foundational capability is the ability to reliably translate natural-language requests into concrete control-system signals. This contribution surveys and systematizes several semantic channel-finding strategies that we have implemented and deployed across multiple accelerator facilities. We present four mature approaches—(1) in-context...
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Fuhao Ji (SLAC National Accelerator Laboratory)22/05/2026, 09:50MC6.D13: Instrumentation: Artificial IntelligenceContributed Oral Presentation
SLAC MeV-UED is part of LCLS scientific user facility and has enabled unprecedented opportunities in the studies of ultrafast structural dynamics in a variety of gas, liquid and solid-state systems. To remain at the scientific and technical forefront, continuing enhancements to the facility and operations are needed. In this talk, we will describe developments of intelligent scientific...
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Prof. Carsten Welsch (University of Liverpool)22/05/2026, 10:10MC6.D13: Instrumentation: Artificial IntelligenceContributed Oral Presentation
The Liverpool Centre for Doctoral Training for Innovation in Data Intensive Science (LIV.INNO) continues to make significant progress in developing precision diagnostics for accelerator facilities. This talk presents recent results from four projects that collectively demonstrate how data-intensive methods, advanced modelling and modern instrumentation can enhance measurements under...
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