Machine learning based phase space tomography using kicked beam turn-by-turn centroid data in a storage ring
Not Available
43 PARTICLE ACCELERATORS↗
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Results for “accelerator beam diagnostics”
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Acceleration of uranium beam to record power of 10.4 kW and observation of new isotopes at Facility for Rare Isotope Beams
The Facility for Rare Isotope Beams (FRIB) is a major nuclear physics facility for research with fast, stopped, and reaccelerated beams that was successfully commissioned in May 2022. A key capability of FRIB is the production of an acceleration of the uranium beam, but this capability requires the facility to work at the design limits of the lowest charge-to-mass ratio and the highest power density on the beam intercepting devices. This paper presents techniques for overcoming the significant challenges in accelerating the uranium beam, culminating in the demonstration of 10.4 kW on target, and the discovery of three new isotopes. The high-power uranium beam enabled us to produce and identify G 88 a , A 93 s , and S 96 e , within the first 24 h of operation. The successful uranium operation at FRIB sets a new record for accelerated uranium beam power above 10 kW and opens a new avenue of research with rare isotopes. Published by the American Physical Society 2024
43 PARTICLE ACCELERATORS↗
Scorpius Accelerator Emittance Diagnostic Design
The 14th International Particle Accelerator Conference (IPAC’23) will be held in Venice, Italy from 7 to 12 May 2023 at the Venice Convention Centre. Website: https://www.ipac23.org/ Presenter: Mark Raphaelian Dear Mark, we acknowledge reception of your abstract #1268 for IPAC'23 - 14th International Particle Accelerator Conference. Here follow the details: Title: The Design of the Emittance Diagnostic for the Scorpius Accelerator Classification: MC6.T03: Beam Diagnostics and Instrumentation Type of presentation: Poster Presentation Submitter: Mark Raphaelian Authors: Mark Raphaelian Co-Authors: Daniel Dzenitis, Franz Weber, Nathaniel Pogue, Sean Littleton, Steven Falabella, Patrick Duffy, William Stem
47 OTHER INSTRUMENTATION↗
Four-dimensional phase-space reconstruction of flat and magnetized beams using neural networks and differentiable simulations
Beams with cross-plane coupling or extreme asymmetries between the two transverse phase spaces are often encountered in particle accelerators. Flat beams with large transverse-emittance ratios are critical for future linear colliders. Similarly, magnetized beams with significant cross-plane coupling are expected to enhance the performance of electron cooling in hadron beams. Preparing these beams requires precise control and characterization of the four-dimensional transverse phase space. In this study, we employ generative phase-space reconstruction techniques to rapidly characterize magnetized and flat-beam phase-space distributions using a conventional quadrupole-scan method. The reconstruction technique is experimentally demonstrated on an electron beam produced at the Argonne Wakefield Accelerator and successfully benchmarked against conventional diagnostics techniques. Specifically, we show that predicted beam parameters from the reconstructed phase-space distributions (e.g., as magnetization and flat-beam emittances) are in excellent agreement with those measured from the conventional diagnostic methods. Published by the American Physical Society 2024
43 PARTICLE ACCELERATORS↗
AI-driven Neutrino Beam Diagnostics for Next-Generation Neutrino Experiments
The accelerator-driven beam uncertainty limits oscillation measurements in long-baseline neutrino experiments. Spill-resolved beam diagnostics and real-time inference are necessary to address these neutrino flux systematics. As such, we present a machine-learning-based beam monitoring framework developed and validated using data from the T2K experiment. Our approach uses downstream, spill-by-spill muon monitor observables to predict upstream parameters such as proton beam position and width. We achieve high predictive accuracy on nominal runs, demonstrating robust baseline performance whether the model is trained on stable runs or systematically varied conditions. The framework is designed to be robust against domain shifts, allowing the neural network architectures and inference strategies developed with T2K data to be retrained and validated using LBNF simulations, with the goal of eventual deployment under real LBNF/DUNE operating conditions. This scalable approach to real-time beam inference offers a pathway toward reducing flux systematics for next-generation neutrino experiments such as DUNE.
Aney, Noah [Fermilab; U. Chicago (main)]↗
Advanced Control Methods for Particle Accelerators (ACM4PA) 2019
Los Alamos is currently developing novel particle accelerator controls and diagnostics algorithms to enable higher quality beams with lower beam losses than is currently possible. The purpose of this workshop was to consider tuning and optimization challenges of a wide range of particle accelerators including linear proton accelerators such as the Los Alamos Neutron Science Center (LANSCE), rings such as the Advanced Photon Source (APS) synchrotron, free electron lasers (FEL) such as the Linac Coherent Light Source (LCLS) and LCLS-II, the European X-ray Free Electron Laser (EuXFEL), the Swiss FEL, and the planned MaRIE FEL, and plasma wake- eld accelerators such as FACET, FACET-II, and AWAKE at CERN. One major challenge is an the ability to quickly create very high quality, extremely intense, custom current and energy pro le beams while working with limited real time non-invasive diagnostics and utilizing time-varying uncertain initial beam distributions and accelerator components. Currently, a few individual accelerator labs have been developing and applying their own diagnostics tools and custom control and ML algorithms for au- tomated machine tuning and optimization. The goal of this workshop was to bring together a group of accelerator physicists and accelerator related control and ML experts in order to de ne which controls and diagnostics would be most useful for existing and future accelerators and to create a plan for developing a new family of algorithms that can be shared and maintained by the community.
43 PARTICLE ACCELERATORS↗
Multi-Moded Multi-Frequency Fast Single Bunch Beam Diagnostic Instrument
Explore the source record for details and available documents.
43 PARTICLE ACCELERATORS↗
Assessment of errors in analytic modeling of permanent magnet electron spectrometers for laser-plasma accelerators
Here, we assess the error in three treatments of a critical electron beam diagnostic for laser-plasma acceleration (LPA) experiments: a permanent magnet electron spectrometer. Since LPA electron spectrometers are often difficult to calibrate due to the scarcity of well-characterized, tunable electron beam sources in the appropriate energy range and the mechanical complexity of electron spectrometers, the standard of calibration is Hall probe measurements. We first compare the electron spectrometer performance between SIMION calculations and the Hall probe measurements. We find up to a 60% (<8%) error in determining the absolute energy for electrons below (above) 80 MeV when using the SIMION-modeled magnetic field vs the measured magnetic field due to SIMION overpredicting fringe field strength. The difference in spectrometer energy resolution is within ∼10% between the modeled and measured fields. We then assess a simple block model, which is commonly used. We find that the simple block model presented here sufficiently captures the predicted as-built spectrometer performance for any application provided that the user can tolerate uncertainties on absolute energy determination up to 5% and errors on energy resolution up to 1%.
Electromagnetic optics↗
Adaptive Machine Learning for Robust Diagnostics and Control of Time-Varying Particle Accelerator Components and Beams
Machine learning (ML) is growing in popularity for various particle accelerator applications including anomaly detection such as faulty beam position monitor or RF fault identification, for non-invasive diagnostics, and for creating surrogate models. ML methods such as neural networks (NN) are useful because they can learn input-output relationships in large complex systems based on large data sets. Once they are trained, methods such as NNs give instant predictions of complex phenomenon, which makes their use as surrogate models especially appealing for speeding up large parameter space searches which otherwise require computationally expensive simulations. However, quickly time varying systems are challenging for ML-based approaches because the actual system dynamics quickly drifts away from the description provided by any fixed data set, degrading the predictive power of any ML method, and limits their applicability for real time feedback control of quickly time-varying accelerator components and beams. In contrast to ML methods, adaptive model-independent feedback algorithms are by design robust to un-modeled changes and disturbances in dynamic systems, but are usually local in nature and susceptible to local extrema. In this work, we propose that the combination of adaptive feedback and machine learning, adaptive machine learning (AML), is a way to combine the global feature learning power of ML methods such as deep neural networks with the robustness of model-independent control. We present an overview of several ML and adaptive control methods, their strengths and limitations, and an overview of AML approaches.
97 MATHEMATICS AND COMPUTING↗
New generation bunch shape monitor for ion accelerators
Measuring longitudinal beam parameters is important for operation and development of high intensity linear accelerators, but it is notoriously difficult for proton and ion beams at non-relativistic energies. The Bunch Shape Monitor (BSM) is a device used for measuring the longitudinal bunch distribution in ion linacs. The existing BSM models have poor electron collection efficiency from the wire and are limited to one-dimensional measurements of the phase coordinate. In response to this problem, we have developed a new generation BSM with improved performance. The proposed design incorporates three major innovations: First, the collection efficiency was improved by adding a focusing field between the wire and the entrance slit, which will also allow measurements over a much wider dynamic range. Second, an improvement in the measurement speed was achieved by sampling longitudinal profiles of multiple energy slices simultaneously, where the BSM wire is placed at the exit of an ion spectrometer so that ions with different energies hit the wire at different horizontal coordinates along the wire. Finally, the design incorporates a motion system that can shift the wire and deflecting cavity together, enabling transverse profile measurements like a wire scanner. Here, in this paper, we will provide the design of the new BSM and report on its beam test results at the Spallation Neutron Source facility in Oak Ridge National Laboratory.
43 PARTICLE ACCELERATORS↗
High-Performance Accelerator Modeling: Toward Improving Controls and Diagnostics for High-Brightness Beams in Experiment
One of the main charges of beam physics is to improve the overall beam quality delivered to experiments and applications, which is generally quantified by the beam brightness. High brightness beams have numerous applications, including x-ray free electron lasers and ultrafast electron diffraction (UED). To these ends, this thesis details efforts to use high-performance models --- high-fidelity models that execute quickly --- for controls and diagnostics. To demonstrate the generality of these techniques, this thesis focuses on three of the most successful photoinjector designs currently used worldwide: the UCLA/SLAC/BNL-type high-gradient S-band gun, the continuous-wave high-repetition rate VHF APEX gun and the L-band DESY-PITZ-type gun. Work will be shown from Pegasus (UCLA), HiRES (LBNL) and FAST (FNAL).Specifically, data-driven models for online virtual diagnostics are presented, in this case, in the context of UED at HiRES, leading to a temporal resolution improvement. Methods for improving the fidelity of physics-based models are discussed, with examples at HiRES, Pegasus and FAST. Markov-chain Monte Carlo analysis is applied to match simulations, in the context of photocathode studies at HiRES and Pegasus. Lastly, the augmentation of online model-based predictions with model-independent optimization is explored in a fluctuating environment at Pegasus and HiRES. A central theme of this dissertation is working with parameter fluctuations when modeling an accelerator beamline. Long-term drifts and shot-to-shot jitter exist in every accelerator to a varying degree and therefore play an important role in every chapter of this dissertation. This thesis attempts to address the issues associated with these fluctuations when trying to develop faithful model representations of the system.
43 PARTICLE ACCELERATORS↗
Longitudinal current profile reconstruction from a wakefield response in plasmas and structures
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72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Collimator irradiation studies in the Argonne Advanced Photon Source at energy densities expected in next-generation storage ring light sources
Not Available
43 PARTICLE ACCELERATORS↗
Four-dimensional emittance measurements of ultrafast electron diffraction optics corrected up to sextupole order
Not Available
43 PARTICLE ACCELERATORS↗
Adaptive model tuning studies for non-invasive diagnostics and feedback control of plasma wakefield acceleration at FACET-II
Beam-driven plasma wakefield acceleration (PWFA) achieves the same energy gain in a single meter, for which conventional accelerators require several kilometers, however much work is still required to match the beam quality of conventional accelerators. The PWFA processes to be studied at the FACET-II facility will utilize extremely short (down to a few fs) high energy (10 GeV), high charge (few nC) electron and eventually positron bunches. The PWFA process is extremely sensitive to the detailed longitudinal current profiles of these bunches and it would be of great benefit to have precise measurement and control of these profiles. We present an adaptive model tuning technique for FACET-II to adaptively tune online models based on real time accelerator and beam data to continuously provide a non-invasive diagnostic which predicts the longitudinal phase space (LPS) of extremely short and highly compressed electron beams, which otherwise require destructive X-band transverse deflecting cavity (XTCAV)-based measurements. Based on simulation studies, our method has the potential for: (1). The development of a non-invasive longitudinal phase space diagnostic by adaptively tuning models based on non-invasive measurements such as energy spread spectra which could be recorded at all of the bunch compressors in the facility. (2). Utilize these diagnostic to perform model-independent feedback-based to achieve desired longitudinal phase space distributions. (3). Utilize the model-independent feedback approach to maximize energy gain while minimizing emittance growth and energy gain variance of the PWFA process by tuning accelerator parameters, while monitoring what longitudinal phase space the algorithm has found, information that will be useful for further analytical and simulation studies.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Coherent Optical Signatures of Electron Microbunching in Laser-Driven Plasma Accelerators
Not Available
43 PARTICLE ACCELERATORS↗
Correcting Magnetic-Field Diffusion Effects in Beam Position Monitors
Beam position monitors (BPMs) provide timeresolved measurements of the current and centroid position of high-current electron beams in linear induction accelerators (LIAs). The data from some types of BPMs can be influenced by magnetic field diffusion into the surrounding metal. We derive an estimate of the correction factor from first principles, and show how it is applied in practice to nearly eliminate the effect from the data.
43 PARTICLE ACCELERATORS↗
Nonlinear integrable optics beam dynamics experiment and diagnostics
All present circular particle accelerators are built around linear focusing optics. Un-avoidable nonlinear elements appear in accelerators, such as magnetic aberrations and space-charge effects, which are typically corrected using nonlinear magnets. However, these added nonlinearities degrade particle beam stability, and in most cases, the system becomes non-integrable. The Integrable Optics Test Accelerator (IOTA), at Fermi National Accelerator Laboratory (Fermilab), is a test bed to study beam dynamics in integrable lattices for both electron and proton beams, independently. This work presents experimental results of the nonlinear electron beam dynamics behavior in IOTA using an elliptical nonlinear magnet. The elliptical nonlinear magnet allows for a large betatron tune spread, allowing for a stable high-intensity beam. In a future experimental run, minimally invasive beam instrumentation is needed to study time-dependent collective instabilities, and halo formation in space-chargedominated proton beams. Traditional profile monitors are too destructive to the beam or just measure one dimension of the beam. A gas sheet can be injected perpendicular to theproton beam at an angle. By doing so, a two-dimensional, turn-by-turn, transverse profile measurement can be made, which will preserve the integrity of the beam. The developmentand progress of a gas sheet beam profile monitor will also be presented.
43 PARTICLE ACCELERATORS↗