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...
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...
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...
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....
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...
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...
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...
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....
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...
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...
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
rings with many quadrupoles, the...
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...
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...
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...
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...
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...
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...
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,...
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,...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
Planned upgrades of the European X-Ray Free Electron Laser (Eu-
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...
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...
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...
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...
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 –...
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.
In this contribution, we show how machine learning played a fundamental role in the design and operational deployment of the Si...
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,...
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,...
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...
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....
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...
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...
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...
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...
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...
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...
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...
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...
Particle accelerators, such as the CERN Linear Electron Accelerator for Research (CLEAR), play a critical role in various scientific fields.
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...
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...
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...
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...
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...
