-
08/04/2025, 08:30
-
Tia Miceli, Verena Kain (CERN)08/04/2025, 09:00
-
Andrea Santamaria Garcia (University of Liverpool), Annika Eichler (DESY)08/04/2025, 09:20Keynote
-
Annika Eichler (DESY)08/04/2025, 09:50Lecture
Control theory is a pivotal field of study that focuses on the behavior of dynamical systems and the development of strategies to influence these systems towards desired outcomes. The principle of control theory find its application in plenty of disciplines including engineering, economics, biology and beyond. It were control concepts like the Kalman filter that has flew the Apollo to the...
Go to contribution page -
Kishan Rajput (Jefferson Lab)08/04/2025, 11:15Lecture
Multi-objective reinforcement learning (MORL) extends traditional reinforcement learning (RL) by addressing environments where multiple conflicting objectives must be optimized simultaneously. In real-world applications, such as autonomous systems, particle accelerator optimization and control, agents often face trade-offs between competing goals. This lecture provides an overview of the key...
Go to contribution page -
Andrea Santamaria Garcia (University of Liverpool), Chenran Xu, Jan Kaiser, Juan Pablo Gonzalez Aguilera (University of Chicago)08/04/2025, 14:00
Many accelerator physics problems, such as beamline design, beam dynamics model calibration, online tuning and phase space measurements rely on solving high-dimensional optimisation problems over beam dynamics simulations. Numerical optimisers have successfully been applied to such tasks, but they struggle as the dimensionality and complexity of the objective function increase. In machine...
Go to contribution page -
wei bao08/04/2025, 15:30
The High Energy Photon Source (HEPS) is a fourth-generation synchrotron radiation facility under construction in Beijing, China. Since the beam commissioning of the storage ring commenced in July 2024, progress has proceeded smoothly, and the first light was achieved in October. During the construction and beam commissioning of HEPS, we explored machine learning to address critical technical...
Go to contribution page -
Sarlota Birnsteinova (European XFEL GmbH)08/04/2025, 15:50
Large-scale facilities like European XFEL consist of a multitude of subsystems, which often require frequent calibration. Additionally, accurate real-time tuning of many of these subsystems is critical to maintain stable and optimal performance. Automation techniques can be leveraged to reduce operators' time investment and potentially increase the exploitation of allotted beamtime, both in...
Go to contribution page -
Nikita Kuklev (Fermilab)08/04/2025, 16:10
The Advanced Photon Source (APS) facility has just completed an upgrade to become one of the world’s brightest storage-ring light sources. For the first time, machine learning (ML) methods have been extensive used as part of the baseline commissioning plan. Most popular such method was Bayesian optimization (BO) – a tool for efficient online high-dimensional single and multi-objective tuning....
Go to contribution page -
Christian Hespe (DESY)08/04/2025, 16:30
Feedback control is an essential component for the successful operation of particle accelerators. However, achieving the desired closed-loop performance requires precise model knowledge, which is difficult to obtain in complex accelerator systems. For this reason, we present an application of a combined optimization approach that estimates the response matrix online while optimizing the chosen...
Go to contribution page -
Xiaolong Chen (Institute of modern physics)08/04/2025, 16:50
Particle accelerators play a critical role in modern scientific research. However, existing manual beam control methods heavily rely on experienced operators, leading to significant time consumption and potential challenges in managing next-generation accelerators characterized by higher beam current and stronger nonlinear properties. In this paper, we establish a dynamical foundation for...
Go to contribution page -
Victoria Isensee09/04/2025, 08:30
Heavy ion synchrotrons, like the SIS18 at GSI, rely on the proven numerical approaches to correct the closed orbit. The SIS18 has a relative moderate amount of BPMs (one per cell) and requires a well corrected and known orbit, especially near the injection/extraction systems. Fluctuations of the BPM signal arise from the electronics. In addition there are systematic errors due to the relative...
Go to contribution page -
Shaun Preston (John Adams Institute, University of Oxford)09/04/2025, 08:50
A general Bayesian optimisation tool is being developed at Diamond Light Source to improve machine performance by constructing surrogates from Gaussian Process (GP) models. Priors are placed on covariance kernel hyperparameters to guide an optimiser and prevent overfitting. The model has been integrated with the machine control system. During an experiment aimed at improving injection...
Go to contribution page -
Verena Kain (CERN)09/04/2025, 09:10
Power supply ripples at various frequencies - characteristic to the magnet circuits or from the electrical network - have always been an issue in accelerator operations, with several mitigation measures put in place over the years. This contribution summarises recent efforts in the CERN SPS to compensate the ripple at 50 Hz and its harmonics in the main quadrupole circuits with ML methods. It...
Go to contribution page -
Andrea De Franco (National Institutes for Quantum Science and Technology (QST))09/04/2025, 09:30
The Linear IFMIF Prototype Accelerator (LIPAc) is designed to accelerate 125 mA of D+ to 9 MeV in CW. The very high power stored in the beam (~1.1 MW) and the use of superconductive RF cavities requires precise control of beam losses (target <1e-6). On the other hand the intense beam is affected by strong space charge forces that easily results in significant halo formation. This contribute is...
Go to contribution page -
Daniel Ratner (SLAC), Zhe Zhang09/04/2025, 09:50
This paper presents a novel application of Multipoint Bayesian Algorithmic Execution (multipointBAX) to optimize dynamic aperture (DA) and momentum aperture (MA) in lattice design. DAMA optimization is a critical design task for storage rings, ultimately determining the flux of x-ray sources and luminosity of colliders. Traditionally, solving this multi-objective optimization problem has...
Go to contribution page -
Ryan Roussel09/04/2025, 10:10
The goal of machine learning for accelerator control is to automate the start-up, optimization, and execution of experiments at accelerator facilities with limited-to-no human operator input. To address this challenge, we have been pursuing a research program to completely automate sequential accelerator beamline configuration tasks at the Argonne Wakefield Accelerator (AWA). These tasks...
Go to contribution page -
Kishansingh Rajput (Jefferson Lab)09/04/2025, 11:00
Particle accelerator operation requires simultaneous optimization of
Go to contribution page
multiple objectives. Multi-Objective Optimization (MOO) is particularly challenging
due to trade-offs between the objectives. Evolutionary algorithms, such as genetic
algorithm (GA), have been leveraged for many optimization problems, however, they
do not apply to complex control problems by design. This paper... -
Armen Kasparian (Jefferson Lab)09/04/2025, 11:20
Complex accelerators must have control systems that can handle dynamic nonlinear environments. This makes traditional control methods unsuitable as they can struggle to adapt to these uncertainties. This provides an ideal environment for reinforcement learning algorithms as they are adaptable and generalizable. We present a reinforcement learning pipeline that can effectively handle the...
Go to contribution page -
75. Explainable physics-based constraints on reinforcement learning for accelerator controls- 15'+5'Jonathan Colen (Old Dominion University)09/04/2025, 11:40
We present a reinforcement learning (RL) framework for controlling particle accelerated experiments that builds explainable physics-based constraints on agent behavior. The goal is to increase transparency and trust by letting users verify that the agent's decision-making process incorporates suitable physics. Our algorithm uses a learnable surrogate function for physical observables, such as...
Go to contribution page -
Eloise Matheson (CERN)09/04/2025, 12:00
Robots are used in the CERN accelerator complex for remote inspections, repairs, maintenance, monitoring, autopsy and quality assurance, to both improve safety and machine availability. Past interventions mostly relied on teleoperation of robotic bases and arms, while some current and many future interventions will use autonomous behaviors, largely based on advances in machine learning and...
Go to contribution page -
Mr Maciej Mleczko (National Synchrotron Radiation Centre)09/04/2025, 12:15
The National Synchrotron Radiation Centre SOLARIS is a third generation light source. SOLARIS, as a big science facility with seven fully operational beamlines, is obligated to provide the best possible conditions for conducting research. One of the ways to create favorable environment is delivering precise tools for teams working across many different fields in SOLARIS. The general problem...
Go to contribution page -
Dr Remi Lehe (LBNL)09/04/2025, 12:30
Laser-plasma acceleration is a promising acceleration technology for a number of applications due to the large accelerating gradient and unique beam properties that it produces. This technology is in active development, and experimental campaigns typically dedicate significant time to exploring the parameter space in real time, adjusting laser properties, target configuration, and other...
Go to contribution page -
Mateusz Leputa09/04/2025, 14:00
Ensuring efficient use of resources and longevity of machine learning projects requires careful consideration of the full machine learning lifecycle especially when models are deployed to interact with live control systems or end users. We present Lume Deployment a framework of standardised modules built for rapid development and deployment of machine learning models and their integration to...
Go to contribution page -
Linh Nguyen (Brookhaven National Laboratory)09/04/2025, 14:20
Plans for the Electron-Ion Collider (EIC), to be built at Brookhaven National Laboratory, include end-to-end and bottom-up capabilities in artificial intelligence (AI) and machine learning (ML). Enabling these capabilities, especially for EIC Operations, will require the large-scale integration of software platforms and tools for the reliable and efficient management of AI/ML-related data,...
Go to contribution page -
Willem Blokland (ORNL)09/04/2025, 14:40
We apply Machine Learning techniques at the Spallation Neutron Source (SNS) to improve operations, specifically to deter and prevent errant beam pulses, to speed up minimization of halo beam losses, and to alert operators to anomalies in the target cooling system. We give an overview of the work done and discuss the infrastructure implemented and under development to support the data...
Go to contribution page -
Seongyeol Kim10/04/2025, 08:30
Generative phase space reconstruction method based on neural networks and differentiable simulations has become a novel beam diagnostic technique to obtain the beam phase space information. Recent studies show that four-dimensional phase space can be successfully obtained by using only YAG images with different quadrupole magnet strength, allowing us to understand both uncoupled and coupled...
Go to contribution page -
Juan Pablo Gonzalez-Aguilera (University of Chicago)10/04/2025, 08:50
Coherent synchrotron radiation (CSR) is a limiting effect in linear accelerators with dispersive elements due to its contribution to projected transverse emittance growth. This effect becomes a limitation for highly compressed beams. Even though CSR-induced projected emittance growth has been widely studied, conventional measurement techniques are not detailed enough to resolve the...
Go to contribution page -
Jonathan Edelen10/04/2025, 09:10
Optics tuning in transfer lines and LINACs can be challenging due to the fact that multiple combinations of machine settings can lead to the same diagnostic output. Moreover, the lack of a periodic solution can limit the ability to infer optics in the same way as rings from BPM signals. Model based approaches are often used to assist with the optics tuning in combination with optimization or...
Go to contribution page -
Malik Marco Algelly (Universite de Geneve (CH))10/04/2025, 09:30
Kicker magnets are essential for particle beam injection and extraction within CERN’s accelerator complex, where high reliability is crucial to maintaining the availability needed for numerous scientific experiments. This study proposes a machine learning approach for forecasting anomalies in these systems, aiming to proactively identify and isolate potential faults before failure occurs. To...
Go to contribution page -
Lynda Boukela (DESY), Burak Dursun (DESY)10/04/2025, 09:50
At the European XFEL, detecting anomalies in superconducting cavities is essential for reliable accelerator performance. We began with a model-based fault detection approach focused on residual analysis to identify anomalies. To improve fault discrimination, particularly for quench events, we augmented this system with machine learning (ML) models. Key challenges included the scarcity of...
Go to contribution page -
Jason Liang (Stanford University)10/04/2025, 10:10
The vast amount of data generated by accelerators makes manual monitoring impractical due to its labor-intensive nature. Existing machine learning solutions often rely on labeled data, manual inspection, and hyperparameter tuning, which limits their scalability. To address these challenges, we leverage coincidence learning—an unsupervised technique designed for multi-modal tasks—to...
Go to contribution page -
Chris Tennant10/04/2025, 11:00
We describe research in deep learning on graph representations of the injector beamline at the Continuous Electron Beam Accelerator Facility (CEBAF) to develop a tool for operations. We leverage operational archived data – both unlabeled and labeled configurations – to train a graph neural network (GNN) via our methods of self-supervised training and supervised fine tuning. We demonstrate the...
Go to contribution page -
Anton Lu (Technische Universität Wien (AT))10/04/2025, 11:20
This work presents a machine learning-based approach for compensating magnetic hysteresis in the main dipole and quadrupole magnets of the multi-cycling CERN SPS, utilizing time series neural architectures like the Temporal Fusion Transformers trained on magnetic field measurements. The predicted magnetic fields enable feed-forward, cycle-by-cycle, compensation through the CERN accelerator...
Go to contribution page -
Paul Stanik III (University of Nevada--Las Vegas)10/04/2025, 11:40
Fast simulations of intense relativistic electron beams can be sufficiently accurate to allow for tuning of an accelerator’s magnetic transport field, but are incapable of capturing all relevant beam physics due to limitations in the model. Because methods that do capture these effects are significantly more computationally-expensive, e.g. particle in-cell simulations, they are fundamentally...
Go to contribution page -
Galo Gallardo Romero (HI Iberia), Guillermo Rodríguez Llorente (Artificial Intelligence Engineer at HI Iberia, Department of Mathematics at UC3M, Gregorio Millán Barbany Institute)10/04/2025, 12:00
A major challenge in constructing future nuclear fusion power plants is understanding how reactor materials are damaged by the neutron flux generated during the fusion process. In order to address this challenge, the IFMIF-DONES neutron source is being built for material irradiation, generating the necessary neutron flux through a stripping reaction between accelerated deuterons and a lithium...
Go to contribution page -
Nikita Kuklev (Fermilab)10/04/2025, 15:30
As the design complexity of modern accelerators grows, there is more interest in using controllable-fidelity simulations that have fast execution time or yield additional insights as compared to standard codes. One notable example of additional information are gradients of physical observables with respect to design parameters produced by differentiable simulations. The IOTA/FAST facility has...
Go to contribution page -
Adwaith Ravichandran (Argonne National Laboratory)10/04/2025, 15:50
A multifaceted virtual accelerator model that seamlessly integrates with the online experimental system would highly benefit the operators to test and evaluate beam tuning scenarios and apply them online. As part of this effort, the beam dynamics code TRACK is wrapped with control system architecture and the graphic user interface BADGER developed by SLAC. Customizability and task...
Go to contribution page -
Eric Cropp10/04/2025, 16:10
Precision control of electron beams is one of the main charges of beam physics, as producing high-brightness beams is critical to numerous accelerator deliverables, including high-quality x-rays from XFELs and high-quality ultrafast probes for UED/UEM. Critical to this effort is a set of accurate system models that can inform control policies. To be useful, these models must accurately...
Go to contribution page -
Qiyuan Xu10/04/2025, 16:30
Beam imaging presents significant challenges due to the necessity of positioning imaging devices near the beam pipe, an area subjected to high levels of radiation that can damage cameras and their peripheral electronics, reducing their lifespan and reliability. With the global discontinuation of radiation-hardened tube cameras previously used for this purpose, a robust and durable replacement...
Go to contribution page -
10/04/2025, 18:15
-
10/04/2025, 22:30
-
Dr Florian Rehm (CERN)11/04/2025, 08:30
This tutorial applies Retrieval Augmented Generation (RAG) as a method to improve documentation retrieval in accelerator physics. Participants will learn how combining information retrieval with generative AI models can provide precise, context-aware answers from vast technical resources. The session includes a hands-on demonstration of implementing RAG in combination with Large Language...
Go to contribution page -
Florian Sohn (European XFEL GmbH)11/04/2025, 09:30
Multimodal Large Language models extend LLMs’ capabilities to input beyond text, often images. At the European XFEL, these models are used as Retrieval-Augmented Generative (RAG) Knowledge assistants in technical and administrative domains. We present a selection of current applications and prototypes: chatbot assistants for data service support, business travel aid, vision-based document...
Go to contribution page -
Thorsten Hellert11/04/2025, 10:20
The specialized terminology and complex concepts inherent in physics present significant challenges for Natural Language Processing (NLP), particularly when relying on general-purpose models. In this talk, I will discuss the development of physics-specific text embedding models designed to overcome these obstacles, beginning with PhysBERT—the first model pre-trained exclusively on a curated...
Go to contribution page -
Raimund Kammering11/04/2025, 10:40
As particle accelerators grow in complexity, traditional control methods face increasing challenges in achieving optimal performance. This paper envisions a paradigm shift: a decentralized multi-agent framework for accelerator control, powered by Large Language Models (LLMs) and distributed among autonomous agents. We present a proposition of a self-improving decentralized system where...
Go to contribution page -
Auralee Edelen, Daniel Ratner (SLAC), Georg Hoffstaetter, Hirokazu Maesaka, Jan Kaiser, Kishan Rajput (Jefferson Lab), Remi Lehe, Ryan Roussel, Thorsten Hellert, Verena Kain (CERN)11/04/2025, 11:00
-
11/04/2025, 13:30
-
Kunxiang Sun (近代物理研究所)
This paper introduces an accelerator surrogate model utilizing Diffusion Models. The proposed model leverages the parameters of the incoming beam bunch as conditional inputs to accurately and efficiently predict the phase space distribution of the beam at the accelerator’s exit. By focusing on the Medium Energy Beam Transport (MEBT) section of the China Accelerator Facility for Superheavy...
Go to contribution page -
Keynote
-
Arne Grünhagen
In pump-probe experiments with free-electron lasers, the arrival time stability between the FEL pump pulse and the probe pulse is of utmost importance. An optical synchronization system is used to synchronize several components of the accelerator and the pump-probe laser. Different seismic activities cause the tunnel length and thus the length of the installed optical fibers to change. In...
Go to contribution page -
Joel Axel Wulff
The CERN Proton Synchrotron (PS) is equipped with several RF systems covering a wide range of revolution harmonics (7 to 21, 42, 84) with heterogeneous hardware of different age. Despite a good track record of high availability (over 99%), a degradation observed from 2021 to 2023 highlighted a need for enhanced fault diagnosis of these RF systems. Accurate identification of failure sources is...
Go to contribution page -
Georg Hoffstaetter (Cornell University)
Accelerator control systems consist of very large numbers of parameters, many of which are continually re-tuned to account for different working conditions and drifting optimal points. In the case of the BNL RHIC Injector Complex, many different ion species are accelerated, and while there is a scarcity of diagnostics, there is a surplus of control knobs for optimizing the injection process....
Go to contribution page -
Yihao GONG (Shanghai Advanced Research Institute)
In modern synchrotron light sources, maintaining beam stability is critical for ensuring high-quality synchrotron radiation performance. Light source stability is governed by stability of current, beam position and beam size. Beam size stability on the order of several microns need to be improved for future experiments. Reinforcement learning (RL) offers a promising approach for real-time beam...
Go to contribution page -
Krzysztof Klimczyk
This project explores methods for automating the creation of device servers in the TANGO Controls environment using Large Language Models (LLMs). The primary goal is to streamline and accelerate the coding process for device servers, reducing the time and effort required by developers. The application features a web-based user interface where users specify device attributes, commands, and...
Go to contribution page -
Matthias Remta (University of Vienna (AT))
Beams designated for the LHC are injected in multiple batches into the SPS. With a tight spacing of 200 ns between these batches, the injection-kickers have to be precisely synchronised with the injected beam so that injection oscillations are minimized. Due to machine drifts the optimal settings for the kickers vary regularly. In this paper a Reinforcement Learning agent was developed as an...
Go to contribution page -
Levente Alex Foldesi (University of Zurich (CH))
The beam for CERN's North Area proton physics program is produced through a Multi-Turn Extraction (MTE) scheme at the Proton Synchrotron (PS). Using fourth-order resonant excitation, the beam is split into five beamlets in horizontal phase space, with extraction occurring over five consecutive turns. The quality of the splitting is measured by the uniformity of intensities across the beamlets....
Go to contribution page -
Sabrina Maria Appel
The complexity of the GSI/FAIR accelerator facility demands a high level of automation to maximize the time for physics experiments. Accelerator laboratories across the globe are investigating numerous techniques to achieve this goal, including classical optimization, Bayesian optimization (BO), and reinforcement learning. This presentation will provide an overview of recent activities in...
Go to contribution page -
Florian Sohn (European XFEL)
The automation of repetitive tasks at FEL light-sources like the European XFEL allows to allocate staff more efficiently and increases reliability and safety. Recurrent procedures often require adjustments of various control parameters to maximize a measured quantity, for example, repositioning of optical components during beam-alignment to maximize intensity. In many cases an analytic form of...
Go to contribution page -
Dr Wei Xu (University of Science and Technology of China)
Beams typically do not travel through the magnet centers because of errors in storage rings. The beam deviating from the quadrupole centers is affected by additional dipole fields due to magnetic field feed-down. Beam-based alignment (BBA) is often performed to determine a golden orbit where the beam circulates around the quadrupole center axes. For storage
Go to contribution page
rings with many quadrupoles, the... -
Mihnea Romanovschi (UKRI/STFC/ISIS)
The controls group at ISIS has been exploring anomaly detection and its associated challenges. This overview highlights the challenges faced, unsuccessful attempts, and lessons learned. Initially, the group implemented a machine learning anomaly detection system on the methane moderator for Target Station 1. The anomaly detection work began before the system upgrade, rendering previous...
Go to contribution page -
Brahim Mustapha
Accelerator design, optimization and simulation, are important steps in the construction of future accelerator-based facilities. Typically, multiple alternatives are investigated, involving multiple design iterations, before selecting a final design. Once a design is selected, the focus shifts to the detailed design of individual accelerator components and end-to-end lattice design and...
Go to contribution page -
Jan Kaiser, Chenran Xu, Andrea Santamaria Garcia (University of Liverpool), Juan Pablo Gonzalez Aguilera (University of Chicago)
Many accelerator physics problems, such as beamline design, beam dynamics model calibration, online tuning and phase space measurements rely on solving high-dimensional optimisation problems over beam dynamics simulations. Numerical optimisers have successfully been applied to such tasks, but they struggle as the dimensionality and complexity of the objective function increase. In machine...
Go to contribution page -
Linh Nguyen
Brookhaven National Laboratory is home to future Electron Ion Collider (EIC). The EIC will collide electrons with protons and nuclei to produce snapshots of particles’ internal structure. This will allow us to study the role of gluons in the matter within and all around us. As the project expands, the growing collection of technical documentation across multiple sub-systems creates challenges...
Go to contribution page -
Chenran Xu (KIT)
Reinforcement learning (RL) is a promising approach for the online control of complex, real-world systems, with recent success demonstrated in applications such as particle accelerator control. However, model-free RL algorithms often suffer from sample inefficiency, making training infeasible without access to high-fidelity simulations or extensive measurement data. This limitation poses a...
Go to contribution page -
Antonin Sulc (DESY)
This work presents a systematic analysis of electronic logbook (eLog) systems and their analytical capabilities at the accelerator facilities of DESY and Lawrence Berkeley National Laboratory (LBNL). We evaluate contemporary tools and methodologies for enhanced information retrieval, focusing on extracting operational insights from eLog entries through state-of-the-art natural language...
Go to contribution page -
Dr Hirokazu Maesaka (RIKEN SPring-8 Center)
An ML-based optimizer has been working on maximizing XFEL performance at SACLA [1]. The spectral brightness of XFEL was successfully optimized by using a new high-resolution inline spectrometer [2]. To improve the XFEL performance further, we plan to enhance beam diagnostics and simulation environments for the ML-based optimizer. As for beam diagnostics, we are developing an X-band RF...
Go to contribution page -
yaxin hu (Institute of Modern Physics)
This study focuses on the time-series prediction problem in beamline simulation for particle linear accelerators, aiming to improve simulation accuracy and computational efficiency by introducing advanced deep learning techniques. We compare and evaluate the performance of several deep learning models, including Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM) networks, Transformer...
Go to contribution page -
Mau-Sen Chiu (National Synchrotron Radiation Research Center)
The APPLE-II types of elliptically polarized undulators (EPU) are extensively utilized in synchrotron light sources. Manufacturing imperfections in the EPU inevitably lead to the creation of a residual skew quadrupole component, which couples horizontal betatron oscillation and dispersion to the vertical plane, consequently altering the vertical beam size. To regulate the vertical beam size,...
Go to contribution page -
Michał Piekarski (NSRC SOLARIS, Jagiellonian University)
The National Synchrotron Radiation Center SOLARIS, third generation light source, is the only synchrotron located in Central-Eastern Europe, in Poland. The SOLARIS Center, with seven fully operational beamlines, serves as a hub for research across a diverse range of disciplines. The most important aspect of such research infrastructure is to provide stable working conditions for the users,...
Go to contribution page -
Georg Hoffstaetter
In BNL’s Booster, the beam bunches can be split into two or three smaller bunches to reduce their space-charge forces. They are then merged back after acceleration in the Alternating Gradient Synchrotron (AGS). This acceleration with decreased space-charge forces can reduce the final emittance, increasing the luminosity in RHIC and improving proton polarization. Parts of this procedure have...
Go to contribution page -
Mahmoud Ajami (European XFEL GmbH)
At the European XFEL, Large Language Models provide opportunities to facilitate technical and administrative workflows, with applications such as chatbots for technical support, travel assistance and coding assistance. A task force was formed to evaluate these applications and to develop a European XFEL AI policy, focusing on technical assessment, framework creation, and ensuring ethical and...
Go to contribution page -
Kathryn Baker, Raunakk Banerjee
Until now, the majority, if not all, accelerator tuning at the ISIS Accelerators has been done manually. With the migration of the control system to EPICS as well as the development of accessible optimization frameworks such as Xopt, we have made the first attempts to automatically tune the Low Energy Beam Transport (LEBT) at ISIS. In this presentation we will discuss the specifics of the...
Go to contribution page -
Jan Kaiser
The photon pulse intensity is one of the key performance metrics of Free Electron Laser (FEL) facilities and has a direct impact on their experimental yield. To date, FEL intensity tuning is a time-consuming manual task that requires expert human operators to have significant skill and experience. Autonomous tuning methods have been demonstrated to reduce setup times and improve the attained...
Go to contribution page -
Mateusz Floras
The National Synchrotron Radiation Centre SOLARIS, a third-generation light source, is the only synchrotron facility in Central-Eastern Europe, located in Poland. The SOLARIS Centre, equipped with seven fully operational beamlines, serves as a key research hub for a wide array of scientific disciplines. The Centre requires advanced software tools to support the analysis of experimental data...
Go to contribution page -
Xiaofeng Gu (Brookhaven National Lab)
Alternating Gradient Synchrotron (AGS) and its Booster serve as part of the injector compound for RHIC and the future EIC at Brookhaven National Laboratory. Injection and early acceleration processes set maximum beam brightness for the collider rings. Such processes have many control parameters and are traditionally optimized empirically by operators. In an effort to streamline the injection...
Go to contribution page -
Jonathan Edelen, Morgan Henderson
Typical operational environments for industrial particle accelerators are less controlled than those of research accelerators. This leads to increased levels of noise in electronic systems, including radio frequency (RF) systems, which make control and optimization more difficult. This is compounded by the fact that industrial accelerators are mass-produced with less attention paid to...
Go to contribution page -
Mr Levente Hajdu (Brookhaven National Laboratory - C-AD Controls)
The Brookhaven Pre-injector Accelerator Facility, which serves RHIC, NSRL, BLIP, and the future EIC, requires occasional tuning of its transfer beam line optics by control room operators to optimize parameters like beam current and emittance. Machine learning (ML) can significantly speed up this tuning process by helping operators quickly identify optimal settings. To facilitate this, ML...
Go to contribution page -
Annika Eichler (DESY)
Control theory is a pivotal field of study that focuses on the behavior of dynamical systems and the development of strategies to influence these systems towards desired outcomes. The principle of control theory find its application in plenty of disciplines including engineering, economics, biology and beyond. It were control concepts like the Kalman filter that has flew the Apollo to the...
Go to contribution page -
Dr Florian Rehm (CERN)
This tutorial applies Retrieval Augmented Generation (RAG) as a method to improve documentation retrieval in accelerator physics. Participants will learn how combining information retrieval with generative AI models can provide precise, context-aware answers from vast technical resources. The session includes a hands-on demonstration of implementing RAG in combination with Large Language...
Go to contribution page -
Jan Kaiser
Autonomous tuning of particle accelerators is an active and challenging research field with the goal of enabling advanced accelerator technologies and cutting-edge high-impact applications, such as physics discovery, cancer research and material sciences. A key challenge with autonomous accelerator tuning remains that the most capable algorithms require experts in optimisation and machine...
Go to contribution page -
Saroj Jena
The Multi-Objective Genetic Algorithm (MOGA) is a powerful and increasingly adopted method for optimizing both linear and nonlinear beam dynamics in accelerator lattices, particularly for ultralow-emittance storage rings. Key objectives in this optimization include minimizing beam emittance for high brightness, maximizing dynamic aperture to ensure efficient particle injection, and expanding...
Go to contribution page -
Amelia Pollard
CLARA is an electron accelerator test facility at STFC’s Daresbury Laboratory in the UK. The first phase has been operated since 2018 for a wide range of accelerator applications. A major upgrade is presently being commissioned that increases the electron beam energy from ~35 MeV to 250 MeV, and adds an experimental area featuring a new high-power laser. Machine learning will play an important...
Go to contribution page -
Javier Martinez Samblas
A key measurement in CERN accelerators for beam diagnostics is transverse size. The Beam Gas Ionization (BGI) instrument enables non-destructive observation of transverse beam profiles by detecting free electrons produced through beam-gas ionization using a Timepix-family detector. However, BGI profiles often suffer from artifacts, such as beam losses, which degrade profile quality and...
Go to contribution page -
Georg Hoffstaetter
Digital twins of particle accelerators are used to plan and control operations and design data collection campaigns. However, a digital twin relies on parameters that are hard to measure directly, e.g., magnet alignments, power supply transfer functions, magnet nonlinearities, and stray fields. These parameters can be constrained by beam position and profile measurements. We use Bayesian...
Go to contribution page -
liu xinzhong
Optical distortion measurement and correction are pivotal for the stable operation of accelerators. This study introduces a machine learning-based approach to optical distortion measurement and correction implemented on the Shanghai Synchrotron Radiation Facility (SSRF). We trained models from modulated orbits to phase advance and from phase advance to quadrupole models, establishing a...
Go to contribution page -
Jonathan Edelen, Morgan Henderson
Neutron scattering experiments are a critical tool for the exploration of molecular structure in compounds. The TOPAZ single crystal diffractometer at the Spallation Neutron Source and the Powder Diffractometer at the High Flux Isotope Reactor study these samples by illuminating them with different energy neutron beams and recording the scattered neutrons. Aligning and maintaining the...
Go to contribution page -
Alexander Klemps (Hamburg University of Technology (TUHH)), Denis Ilia (DESY)
Planned upgrades of the European X-Ray Free Electron Laser (Eu-
Go to contribution page
XFEL) target higher photon energy and a high duty-cycle operation up to CW-
operation using a superconducting RF gun with lower gradient. An operation in
this regime though critically depends on improvements of the beam slice emit-
tance of the electron gun. Within the OPAL-FEL project, we are addressing this
challenge by... -
Yue Sun (Deutsches Elektronen-Synchrotron DESY)
Superconducting radio frequency (SRF) cavities are critical components in particle accelerators, where accurately calibrated RF signals are essential for assessing cavity bandwidth and detuning, providing key insights into cavity performance and facilitating optimal accelerator operation. In practice, however, calibration drift due to humidity and temperature fluctuations over time poses a...
Go to contribution page -
Lynda Boukela
Superconducting Radio Frequency (SRF) cavities are essential components in particle accelerators, where quenches, which can cause an abrupt loss of superconductivity, remain a significant challenge. While quench detection has traditionally relied on single-parameter analysis, these methods are often limited in terms of robustness and scalability. In this work, we explore the use of machine...
Go to contribution page -
Dr Lijuan Yang (Institute of Modern Physics, Chinese Academy of Sciences)
In 2021, the Chinese ADS Front-end demo superconducting radio-frequency (SRF) linac, known as CAFe, successfully conducted a commissioning of a 10 mA, 200 kW continuous wave proton beam. During this commissioning, it was observed that the SRF faults are the leading causes of short machine downtime trips, contributing to approximately 70% of total beam trips. Analyzing fault data and...
Go to contribution page -
Vasiliki Stergiou (University of Oxford (GB))
In the SPS, a flexible machine serving the LHC and a multitude of fixed-target experiments and fast-extraction facilities, reliable monitoring of the transverse beam position across a wide range of different beam structures and intensities is essential for stable and efficient operation. Today, the calibration procedure and signal processing of the beam position monitors (BPMs) of the SPS –...
Go to contribution page -
Francesco Maria Velotti (CERN)
The CERN-SPS slow extraction has been recently equipped with a silicon bent crystal to reduce losses at the electrostatic septum (ES) wires. Such a concept exploits the coherent deflection that a thin crystal can give to part of the separatrix to avoid the ES wires.
Go to contribution page
In this contribution, we show how machine learning played a fundamental role in the design and operational deployment of the Si... -
Nikolina Bunijevac
In times of concern over the environmental impact of high-energy physics organizations, our research in CERN's Cooling and Ventilation group (EN/CV) investigates energy-saving strategies for heating, ventilation, and air conditioning (HVAC) systems. Widely used in both residential and industrial settings, HVAC systems contribute up to 40% of residential and 70% of industrial consumption,...
Go to contribution page -
Prof. Chong Shik Park (Korea University)
This study explores a multi-objective optimization and modeling approach for enhancing the performance of a linear accelerator (linac) used in Fourth Generation Synchrotron Radiation (4GSR) facilities, focusing on the minimization of horizontal and vertical emittances as well as energy spread at the linac end. Efficient control of these parameters is critical to achieving high beam quality,...
Go to contribution page -
Borja Rodriguez Mateos (Universitat Politecnica Catalunya (ES))
Aging of the stripper foil and unexpected machine shutdowns are the primary causes for reduction of the injected intensity from CERN’s linac3 into the Low Energy Ion Ring (LEIR). As a result, the set of optimal control parameters that maximizes beam intensity in the ring tends to drift, requiring daily adjustments to the machine control settings. In this paper, several data-driven methods such...
Go to contribution page -
-
Prof. Chong Shik Park (Korea University)
This study investigates the application of machine learning techniques for the phase space reconstruction of heavy ion linac beams at the Rare isotope Accelerator complex for ON-line experiments (RAON) facility in Korea. Phase space analysis is a critical component in understanding and optimizing beam dynamics, enabling precise control of beam quality for advanced nuclear physics experiments....
Go to contribution page -
Poster
-
Isabella Vojskovic
This study explores various neural network approaches to simulate beam dynamics, specifically addressing non-linear space charge effects. We introduce a convolutional encoder-decoder architecture with skip connections, achieving a relative error of 0.5% in predicting both transversal and coupled 3D electric self-fields. Additionally, to enhance interpretability and robustness, we investigate...
Go to contribution page -
Andrea Vella (University of Malta (MT))
The Large Hadron Collider (LHC) operates with high intensity proton and heavy ion beams that necessitate a robust collimation system to prevent damage to sensitive equipment along the ring. However, the efficiency of cleaning ion beams is approximately 100 times less efficient than with protons. To address this, bent silicon crystals were implemented to enhance collimation efficiency. The...
Go to contribution page -
Zhijun wang (IMP,CAS)
The long-term stable operation of high-power accelerators demands high availability, reliability, and safety, requirements that traditional labor-intensive maintenance methods struggle to meet. In recent years, with the rapid advancement of artificial intelligence and machine learning technologies, various efficient algorithms have demonstrated great potential in the field of particle...
Go to contribution page -
Alexander Nicholas Jury (University of Liverpool (GB))
The Beam Synchrotron Radiation Longitudinal density monitor (BSRL) at the LHC leverages time-correlated single-photon counting to provide high-dynamic-range measurements of particle populations within each bunch in the LHC including monitoring of “ghost” and “satellite” bunches, which represent charge captured in nominally empty buckets, thereby enhancing the accuracy of luminosity...
Go to contribution page -
Dominic Schneider (Institut für Kernphysik, TU Darmstadt)
Machine learning methods provide a significant potential for the optimized operation of complex facilities, such as particle accelerators. In this contribution, the first training and application of surrogate models to the electron accelerator S-DALINAC based on Fully-Connected Neural Networks (FCNN) will be presented.
An exhaustive data-mining algorithm has been developed to generate the...
Go to contribution page -
Chunguang Su (Institute of modern physics, Chinese Academy of Sciences)
Beam tuning in particle accelerators is a complex task, especially when physical modeling is impractical due to the lack of complete beam diagnostics. Traditional methods often rely on iterative manual tuning by operators, which can be inefficient. Reinforcement learning (RL) algorithms offer a promising alternative for automating this process. In this work, we demonstrate the successful...
Go to contribution page -
Stamatina Detsi (National Technical University of Athens, School of Electrical and Computer Engineering)
The Proton Synchrotron (PS) at CERN is equipped with numerous RF systems allowing for evolved longitudinal beam manipulations to adapt the number of bunches and their spacing. The beam produced for the LHC undergoes several bunch splittings, merging and batch compression. Each manipulation must be carefully adjusted to minimize the spread in bunch parameters at PS extraction. The design of...
Go to contribution page -
Fady Bishara (European XFEL GmbH)
The European XFEL is a scientific research facility that produces ultra-short and ultra-brilliant x-ray pulses. The facility is 3.4 kilometers long and comprises, very simplistically, three main sections: a linear electron accelerator, undulators, and photon beamlines. The entire facility is densely instrumented with various diagnostic devices that produce a vast amount of diverse...
Go to contribution page -
Francisco Huhn (CERN)
The Proton Synchrotron Booster (PSB) receives 160 MeV H$^-$ ions, which are converted to protons at injection via a charge exchange mechanism, an upgrade that allows the production of high-intensity beams ($> 10^{13}$ per ring). Nevertheless, with the increase in intensity, space-charge losses arise. To mitigate these effects, horizontal phase-space painting is performed with a system of fours...
Go to contribution page -
89. Toward Autonomous Control: Reinforcement Learning for Improving accelerator performance in CLEARAntonio Gilardi
Particle accelerators, such as the CERN Linear Electron Accelerator for Research (CLEAR), play a critical role in various scientific fields.
Go to contribution page
Ensuring their operation is automatic, stable, and reproducible is vital for the scalability of future large-scale accelerator projects.
This paper presents an initial step toward autonomous control of the CLEAR beamline, beginning with a basic beam... -
Chenran Xu, Jan Kaiser
Reinforcement learning (RL) has been successfully applied to various online tuning tasks, often outperforming traditional optimization methods. However, model-free RL algorithms typically require a high number of samples, with training processes often involving millions of interactions. As this time-consuming process needs to be repeated to train RL-based controllers for each new task, it...
Go to contribution page -
Ryan Roussel
The Xopt/Badger ecosystem offers a versatile suite of tools designed to address the growing needs of advanced optimization and online control in scientific applications. The goal of these tools is to standardize the implementation and use of advanced optimization algorithms at arbitrary scientific facilities for the benefit of the wider accelerator community. In this work, we provide a summary...
Go to contribution page -
Kathryn Baker
Gaussian Processes are the most popular and accurate modelling technique for estimating uncertainty in a system. However, they struggle to scale with high dimensional data and large datasets that are often required to train surrogate models of accelerator components. Typical alternatives include deep ensembles, Monte-Carlo Dropout and quantile regression. However, each of these methods has...
Go to contribution page -
Kathryn Baker, Raunakk Banerjee
Tuning of the accelerator at the ISIS Neutron and Muon Source has traditionally been done manually, relying on experts to vary parameters in the control system to achieve optimum beam efficiency and intensity. Bayesian Optimisation (BO) is an effective way to automatically tune the Low Energy Beam Transport (LEBT), a section within the linear accelerator. However, due to losses and radiation...
Go to contribution page
Choose timezone
Your profile timezone: