Paper | Title | Other Keywords | Page |
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MOPV048 | Fast Multipole Method (FMM)-Based Particle Accelerator Simulations in the Context of Tune Depression Studies | multipole, space-charge, hadron, HOM | 271 |
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Funding: U.S. Department of Energy DOE SBIR Phase I Project DE-SC0020934 As part of the MACH-B (Multipole Accelerator Codes for Hadron Beams) project, we have developed a Fast Multipole Method (FMM**)-based tool for higher fidelity modeling of particle accelerators for high-energy physics within Fermilab’s Synergia* simulation package. We present results from our implementations with a focus on studying the difference between tune depression estimates obtained using PIC codes for computing the particle interactions and those obtained using FMM-based algorithms integrated within Synergia. In simulating the self-interactions and macroparticle actions necessary for accurate simulations, we present a newly-developed kernel inside of a kernel-independent FMM in which near-field kernels are modified to incorporate smoothing while still maintaining consistency at the boundary of the far-field regime. Each simulation relies on Synergia with one major difference: the way in which particles interactions were computed. Specifically, following our integration of the FMM into Synergia, changes between PIC-based computations and FMM-based computations are made by changing only the method for near-field (and self) particle interactions. * J. Amundson et al. "Synergia: An accelerator modeling tool with 3-D space charge". J.C.P. 211.1 (2006) 229-248. ** L. Greengard. "Fast algorithms for classical physics". Science (Aug 1994) 909-914. |
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DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-MOPV048 | ||
About • | Received ※ 09 October 2021 Revised ※ 20 October 2021 Accepted ※ 20 November 2021 Issue date ※ 29 December 2021 | ||
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WEPV020 | Learning to Lase: Machine Learning Prediction of FEL Beam Properties | network, diagnostics, FEL, electron | 677 |
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Accurate prediction of longitudinal phase space and other properties of the electron beam are computationally expensive. In addition, some diagnostics are destructive in nature and/or cannot be readily accessed. Machine learning based virtual diagnostics can allow for the real-time generation of longitudinal phase space and other graphs, allowing for rapid parameter searches, and enabling operators to predict otherwise unavailable beam properties. We present a machine learning model for predicting a range of diagnostic screens along the accelerator beamline of a free-electron laser facility, conditional on linac and other parameters. Our model is a combination of a conditional variational autoencoder and a generative adversarial network, which generates high fidelity images that accurately match simulation data. Work to date is based on start-to-end simulation data, as a prototype for experimental applications. | |||
Poster WEPV020 [1.330 MB] | |||
DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-WEPV020 | ||
About • | Received ※ 10 October 2021 Revised ※ 22 October 2021 Accepted ※ 28 December 2021 Issue date ※ 25 February 2022 | ||
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WEPV024 | X-Ray Beamline Control with Machine Learning and an Online Model | controls, software, radiation, synchrotron | 695 |
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Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under contract DE-SC0020593. We present recent developments on control of x-ray beamlines for synchrotron light sources. Effective models of the x-ray transport are updated based on diagnostics data, and take the form of simplified physics models as well as learned models from scanning over mirror and slit configurations. We are developing this approach to beamline control in collaboration with several beamlines at the NSLS-II. By connecting our online models to the Blue-Sky framework, we enable a convenient interface between the operating machine and the model that may be applied to beamlines at multiple facilities involved in this collaborative software development. |
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DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-WEPV024 | ||
About • | Received ※ 10 October 2021 Accepted ※ 21 November 2021 Issue date ※ 17 December 2021 | ||
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WEPV048 | An Archiver Appliance Performance and Resources Consumption Study | network, EPICS, controls, software | 774 |
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At the European Spallation Source (ESS), 1.6 million signals are expected to be generated by a (distributed) control layer composed of around 1500 EPICS IOCs. A substantial amount of these signals - i.e. PVs - will be stored by the Archiving Service, a service that is currently under development at the Integrated Control System (ICS) Division. From a technical point of view, the Archiving Service is implemented using a software application called the Archiver Appliance. This application, originally developed at SLAC, records PVs as a function of time and stores these in its persistent layer. A study based on multiple simulation scenarios that model ESS (future) modus operandi has been conducted by ICS to understand how the Archiver Appliance performs and consumes resources (e.g. RAM) under disparate workloads. This paper presents: 1) The simulation scenarios; 2) The tools used to collect and interpret the results; 3) The storage study; 4) The retrieval study; 5) The resources saturation study; 6) Conclusions based on the interpretation of the results. | |||
Poster WEPV048 [0.487 MB] | |||
DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-WEPV048 | ||
About • | Received ※ 10 October 2021 Accepted ※ 11 February 2022 Issue date ※ 12 March 2022 | ||
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THAL01 | Machine Learning Tools Improve BESSY II Operation | experiment, network, ISOL, controls | 784 |
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At the HZB user facility BESSY II Machine Learning (ML) technologies aim at advanced analysis, automation, explainability and performance improvements for accelerator and beamline operation. The development of these tools is intertwined with improvements of the prediction part of the digital twin instances at BESSY II [*] and the integration into the Bluesky Suite [**,***]. On the accelerator side, several use cases have recently been identified, pipelines designed and models tested. Previous studies applied Deep Reinforcement Learning (RL) to booster current and injection efficiency. RL now tackles a more demanding scenario: the mitigation of harmonic orbit perturbations induced by external civil noise sources. This paper presents methodology, design and simulation phases as well as challenges and first results. Further ML use cases under study are, among others, anomaly detection prototypes with anomaly scores for individual features.
[*] P. Schnizer et. al, IPAC21 [**] D. Allan, T. Caswell, S. Campbell and M. Rakitin, Synchrot. Radiat. News 32 19-22, 2019 [***] W. Smith et. al, this conference |
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Slides THAL01 [9.849 MB] | |||
DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-THAL01 | ||
About • | Received ※ 08 October 2021 Revised ※ 24 October 2021 Accepted ※ 21 November 2021 Issue date ※ 29 January 2022 | ||
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THAL02 | Bayesian Techniques for Accelerator Characterization and Control | experiment, target, controls, solenoid | 791 |
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Funding: This work was supported by the U.S. National Science Foundation under Award No. PHY-1549132, the Center for Bright Beams. Accelerators and other large experimental facilities are complex, noisy systems that are difficult to characterize and control efficiently. Bayesian statistical modeling techniques are well suited to this task, as they minimize the number of experimental measurements needed to create robust models, by incorporating prior, but not necessarily exact, information about the target system. Furthermore, these models inherently take into account noisy and/or uncertain measurements and can react to time-varying systems. Here we will describe several advanced methods for using these models in accelerator characterization and optimization. First, we describe a method for rapid, turn-key exploration of input parameter spaces using little-to-no prior information about the target system. Second, we highlight the use of Multi-Objective Bayesian optimization towards efficiently characterizing the experimental Pareto front of a system. Throughout, we describe how unknown constraints and parameter modification costs are incorporated into these algorithms. |
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Slides THAL02 [4.453 MB] | |||
DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-THAL02 | ||
About • | Received ※ 10 October 2021 Revised ※ 10 November 2021 Accepted ※ 21 November 2021 Issue date ※ 26 December 2021 | ||
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THAL04 | Machine Learning Based Tuning and Diagnostics for the ATR Line at BNL | quadrupole, network, controls, diagnostics | 803 |
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Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under Award Number DE-SC0019682. Over the past several years machine learning has increased in popularity for accelerator applications. We have been exploring the use of machine learning as a diagnostic and tuning tool for transfer line from the AGS to RHIC at Brookhaven National Laboratory. In our work, inverse models are used to either provide feed-forward corrections for beam steering or as a diagnostic to illuminate quadrupole magnets that have excitation errors. In this talk we present results on using machine learning for beam steering optimization for a range of different operating energies. We also demonstrate the use of inverse models for optical error diagnostics. Our results are from studies that use combine simulation and measurement data. |
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Slides THAL04 [4.845 MB] | |||
DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-THAL04 | ||
About • | Received ※ 10 October 2021 Revised ※ 22 October 2021 Accepted ※ 06 February 2022 Issue date ※ 01 March 2022 | ||
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THPV047 | Status of High Level Application Development for HEPS | controls, MMI, software, framework | 978 |
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The High Energy Photon Source (HEPS) is a 6 GeV, 1.3 km, ultralow emittance ring-based light source in China. The construction started in 2019. In this year, the development of beam commissioning software of HEPS started. It was planned to use EPICS as the control system and Python as the main development tools for high level applications (HLAs). Python has very rich and mature modules to meet the challenging requirements of HEPS commissioning and operation, such as PyQt5 for graphical user interface (GUI) application development, PyEPICS and P4P for communicating with EPICS. A client-server framework was proposed for online calculations and always-running programs. Model based control is also one important design criteria, all the online commissioning software should be easily connected to a powerful virtual accelerator (VA) for comparison and predicting actual beam behaviour. It was planned to use elegant and Ocelot as the core calculation model of VA | |||
DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-THPV047 | ||
About • | Received ※ 10 October 2021 Revised ※ 20 October 2021 Accepted ※ 21 November 2021 Issue date ※ 26 February 2022 | ||
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FRBR02 | An Integrated Data Processing and Management Platform for X-Ray Light Source Operations* | experiment, interface, real-time, GUI | 1059 |
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Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research under Award Number DE-SC00215553. The design, execution, and analysis of light source experiments requires the use of increasingly complex simulation, controls and data management tools. Existing workflows require significant specialization to account for beamline-specific operations and pre-processing steps in order to collect and prepare data for more sophisticated analysis. Recent efforts to address these needs at the National Synchrotron Light Source II (NSLS-II) have resulted in the creation of the Bluesky data collection framework*, an open-source library providing for experimental control and scientific data collection via high level abstraction of experimental procedures, instrument readouts, and data analysis. We present a prototype data management interface that couples with Bluesky to support guided simulation, measurement, and rapid processing operations. Initial demonstrations illustrate application to coherent X-ray scattering beamlines at the NSLS-II. We then discuss extensions of this interface to permit analysis operations across distributed computing resources, including the use of the Sirepo scientific framework, as well as Jupyter notebooks running on remote computing clusters**. * M.S. Rakitin et al., Proc. SPIE 11493, Advances in Computational Methods for X-Ray Optics V, p. 1149311, Aug 2020. ** M.S. Rakitin et al., Journal of Synchrotron Radiation, vol. 25, pp. 1877-1892, Nov 2018. |
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Slides FRBR02 [8.627 MB] | |||
DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-FRBR02 | ||
About • | Received ※ 21 October 2021 Revised ※ 27 October 2021 Accepted ※ 20 November 2021 Issue date ※ 24 January 2022 | ||
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