Keyword: network
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MOP10 Removing Noise in BPM Measurements with Variational Autoencoders operation, optics, coupling, controls 43
  • J.P. Edelen, J.A. Einstein-Curtis, C.C. Hall, M.J. Henderson
    RadiaSoft LLC, Boulder, Colorado, USA
  • A.L. Romanov
    Fermilab, Batavia, Illinois, USA
  Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of High Energy Physics under Award Number DE-SC0021699.
Noise in beam measurements is an ever-present challenge in accelerator operations. In addition to the challenges presented by hardware and signal processing, new operational regimes, such as ultra-short bunches, create additional difficulties in routine beam measurements. Techniques in machine learning have been successfully applied in other domains to overcome challenges inherent in noisy data. Variational autoencoders (VAEs) are shown to be capable of removing significant leevels of noise. A VAE can be used as a pre-processing tool for noise removal before the de-noised data is analyzed via other methods, or the VAE can be directly used to make beam dynamics measurements. Here we present the use of VAEs as a tool for addressing noise in BPM measurements.
DOI • reference for this paper ※ doi:10.18429/JACoW-IBIC2022-MOP10  
About • Received ※ 29 August 2022 — Revised ※ 10 September 2022 — Accepted ※ 11 September 2022 — Issue date ※ 24 November 2022
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MOP38 Beam Profile Monitoring and Distributed Analysis Using the RabbitMQ Message Broker interface, software, controls, Ethernet 140
  • D. Proft, K. Desch, D. Elsner, F. Frommberger, S. Kronenberg, A. Spreitzer, M.T. Switka
    ELSA, Bonn, Germany
  The ELSA facility utilizes several digital cameras for beam profile measurements on luminous screens and synchrotron radiation monitors. Currently a multitude of devices with analog signal output are being replaced in favor of digital outputs, preferably with data transfer via Ethernet. The increased network traffic for streaming, analyzing, and distribution of processed data to control system and machine operators is managed through a supplementary camera network in which distributed computing is performed by the RabbitMQ message broker. This allows performant and platform-independent image acquisition from multiple cameras, real time profile analysis, and supports programming interfaces for C++ and Python. The setup and performance of the implementation are presented.  
DOI • reference for this paper ※ doi:10.18429/JACoW-IBIC2022-MOP38  
About • Received ※ 07 September 2022 — Revised ※ 09 September 2022 — Accepted ※ 12 September 2022 — Issue date ※ 28 October 2022
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MOP43 Web-Based Application for Cable Simulation Models simulation, instrumentation, electron, impedance 156
  • M.C. Paniccia, S.L. Clark, D.M. Gassner, R.L. Hulsart, P. Thieberger
    BNL, Upton, New York, USA
  Attenuation in a lossy coaxial cable increases over distance and varies over frequency. Having a model of these variations can help predict the expected loss and distortion of a signal. This paper discusses a free web-based application developed to provide accurate SPICE models for various coaxial cable types. The user can specify a length and select between different cable types, or upload their own cable attenuation curve, and receive a SPICE model for that cable. These simulation models have been used to assist the design and development of new instrumentation systems for the future Electron Ion Collider (EIC).  
DOI • reference for this paper ※ doi:10.18429/JACoW-IBIC2022-MOP43  
About • Received ※ 06 September 2022 — Revised ※ 10 September 2022 — Accepted ※ 13 September 2022 — Issue date ※ 22 November 2022
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TUP38 Deep Neural Network for Beam Profile Classification in Synchrotron diagnostics, operation, emittance, synchrotron 323
  • M. Piekarski
    NSRC SOLARIS, Kraków, Poland
  Funding: The presented work has been achieved in collaboration with AGH University of Science and Technology in Kraków as a part of a PhD thesis.
The main goal of NSRC SO­LARIS is to pro­vide sci­en­tific com­mu­nity with high qual­ity syn­chro­tron light. To achieve this, it is necessary to constantly monitor many subsystems responsible for beam stability and to analyze data about the beam itself from various diagnostic beamlines. In this work a deep neural net­work for transverse beam profile classification is pro­posed. Main task of the system is to automatically assess and classify transverse beam profiles based solely on the evaluation of the beam image from the Pinhole diagnostic beamline at SOLARIS. At the present stage, a binary assignment of each profile is performed: stable beam operation or unstable beam operation / no beam. Base model architecture consists of a pre-trained con­vo­lu­tional neural net­work followed by a densely-connected classifier and the system reaches accuracy at the level of 90%. The model and the results obtained so far are discussed, along with plans for future development.
poster icon Poster TUP38 [0.376 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-IBIC2022-TUP38  
About • Received ※ 30 August 2022 — Revised ※ 10 September 2022 — Accepted ※ 12 September 2022 — Issue date ※ 15 October 2022
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TUP39 Neural Network Inverse Models for Implicit Optics Tuning in the AGS to RHIC Transfer Line quadrupole, diagnostics, operation, controls 327
  • J.P. Edelen, N.M. Cook, J.A. Einstein-Curtis
    RadiaSoft LLC, Boulder, Colorado, USA
  • K.A. Brown, V. Schoefer
    BNL, Upton, New York, USA
  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
One of the fundamental challenges of using machine-learning-based inverse models for optics tuning in accelerators, particularly transfer lines, is the degenerate nature of the magnet settings and beam envelope functions. Moreover, it is challenging, if not impossible, to train a neural network to compute correct quadrupole settings from a given set of measurements due to the limited number of diagnostics available in operational beamlines. However, models that relate BPM readings to corrector settings are more forgiving, and have seen significant success as a benchmark for machine learning inverse models. We recently demonstrated that when comparing predicted corrector settings to actual corrector settings from a BPM inverse model, the model error can be related to errors in quadrupole settings. In this paper, we expand on that effort by incorporating inverse model errors as an optimization tool to correct for optics errors in a beamline. We present a toy model using a FODO lattice and then demonstrate the use of this technique for optics corrections in the AGS to RHIC transfer line at BNL.
DOI • reference for this paper ※ doi:10.18429/JACoW-IBIC2022-TUP39  
About • Received ※ 05 September 2022 — Revised ※ 10 September 2022 — Accepted ※ 11 September 2022 — Issue date ※ 12 November 2022
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TUP42 Fast Orbit Feedback Upgrade at SOLEIL interface, FPGA, controls, electron 339
  • R. Broucquart, N. Hubert
    SOLEIL, Gif-sur-Yvette, France
  In the framework of the SOLEIL II project, the diagnostics group must anticipate ahead of the dark period the upgrade of important system like the BPM electronics, the timing system end the Fast Orbit Feedback (FOFB). The FOFB is a complex system that is currently embedded in the BPM electronics modules (eBPM). A new flexible stand-alone platform is under conception to follow the future upgrades of surrounding equipment, and to allow the integration of future correction schemes. In this paper we will present the current status of technical decisions, tests and developments.  
poster icon Poster TUP42 [3.305 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-IBIC2022-TUP42  
About • Received ※ 07 September 2022 — Revised ※ 10 September 2022 — Accepted ※ 12 September 2022 — Issue date ※ 25 September 2022
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WEP40 A Modern Ethernet Data Acquisition Architecture for Fermilab Beam Instrumentation Ethernet, software, instrumentation, data-acquisition 500
  • R.R. Santucci, J.S. Diamond, N. Eddy, A. Semenov, D.C. Voy
    Fermilab, Batavia, Illinois, USA
  The Fermilab Accelerator Division, Instrumentation Department is adopting an open-source framework to replace our embedded VME-based data acquisition systems. Utilizing an iterative methodology, we first moved to embedded Linux, removing the need for VxWorks. Next, we adopted Ethernet on each data acquisition module eliminating the need for the VME backplane in addition to communicating with a rack mount server. Development of DDCP (Distributed Data Communications Protocol), allowed for an abstraction between the firmware and software layers. Each data acquisition module was adapted to read out using 1GbE and aggregated at a switch which up linked to a 10GbE network. Current development includes scaling the system to aggregate more modules, to increase bandwidth to support multiple systems and to adopt MicroTCA as a crate technology. The architecture was utilized on various beamlines around the Fermilab complex including PIP2IT, FAST/IOTA and the Muon Delivery Ring. In summary, we were able to develop a data acquisition framework which incrementally replaces VxWorks & VME hardware as well as increases our total bandwidth to 10Gbit/s using off the shelf Ethernet technology.  
poster icon Poster WEP40 [0.738 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-IBIC2022-WEP40  
About • Received ※ 08 September 2022 — Revised ※ 10 September 2022 — Accepted ※ 12 September 2022 — Issue date ※ 04 October 2022
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WEP42 Application of Machine Learning towards Particle Counting and Identification Windows, extraction, detector, experiment 508
  • S.E. Engel
    University of Essex, Physics Centre, Colchester, United Kingdom
  • P. Boutachkov, R. Singh
    GSI, Darmstadt, Germany
  An exploration into the application of three machine learning (ML) approaches to identify and separate events in the detectors used for particle counting at the GSI Helmholtz Centre for Heavy Ion Research. A convolutional neural network (CNN), a shape-based template matching algorithm (STMF) and Peak Property-based Counting Algorithm (PPCA) were developed to accurately count the number of particles without domain-specific knowledge required to run the currently used algorithm. The three domain-agnostic ML algorithms are based on data from scintillation counters commonly used in beam instrumentation and represent proof-of-work for an automated particle counting system. The algorithms were trained on a labelled set of over 150 000 experimental particle data. The results of the three classification approaches were compared to find a solution that best mitigates the effects of particle pile-ups. The two best-achieving algorithms were the CNN and PPCA, achieving an accuracy of 99.8\%.
This project has received funding from the European Union’s Horizon 2020 Research and Innovation programme under GA No 101004730.
poster icon Poster WEP42 [1.370 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-IBIC2022-WEP42  
About • Received ※ 11 September 2022 — Revised ※ 25 October 2022 — Accepted ※ 01 December 2022 — Issue date ※ 08 December 2022
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