Keyword: real-time
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MOPA15 Synchronous High-Frequency Distributed Readout for Edge Processing at the Fermilab Main Injector and Recycler distributed, controls, Ethernet, operation 79
 
  • J.R. Berlioz, J.M.S. Arnold, M.R. Austin, P.M. Hanlet, K.J. Hazelwood, M.A. Ibrahim, A. Narayanan, D.J. Nicklaus, G. Pradhan, A.L. Saewert, B.A. Schupbach, R.M. Thurman-Keup, N.V. Tran
    Fermilab, Batavia, Illinois, USA
  • J. Jiang, H. Liu, S. Memik, R. Shi, M. Thieme, D. Ulusel
    Northwestern University, Evanston, Illinois, USA
  • A. Narayanan
    Northern Illinois University, DeKalb, Illinois, USA
 
  Funding: Operated by Fermi Research Alliance, LLC under Contract No.De-AC02-07CH11359 with the United States Department of Energy. Additional funding provided by Grant Award No. LAB 20-2261
The Main Injector (MI) was commissioned using data acquisition systems developed for the Fermilab Main Ring in the 1980s. New VME-based instrumentation was commissioned in 2006 for beam loss monitors (BLM), which provided a more systematic study of the machine and improved displays of routine operation. However, current projects are demanding more data and at a faster rate from this aging hardware. One such project, Real-time Edge AI for Distributed Systems (READS), requires the high-frequency, low-latency collection of synchronized BLM readings from around the approximately two-mile accelerator complex. Significant work has been done to develop new hardware to monitor the VME backplane and broadcast BLM measurements over Ethernet, while not disrupting the existing operations-critical functions of the BLM system. This paper will detail the design, implementation, and testing of this parallel data pathway.
 
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DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA15  
About • Received ※ 03 August 2022 — Revised ※ 04 August 2022 — Accepted ※ 14 August 2022 — Issue date ※ 19 August 2022
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MOPA28 Semantic Regression for Disentangling Beam Losses in the Fermilab Main Injector and Recycler operation, distributed, proton, beam-losses 112
 
  • M. Thieme, H. Liu, S. Memik, R. Shi
    Northwestern University, Evanston, Illinois, USA
  • J.M.S. Arnold, M.R. Austin, P.M. Hanlet, K.J. Hazelwood, M.A. Ibrahim, V.P. Nagaslaev, A. Narayanan, D.J. Nicklaus, G. Pradhan, A.L. Saewert, B.A. Schupbach, K. Seiya, R.M. Thurman-Keup, N.V. Tran
    Fermilab, Batavia, Illinois, USA
 
  Funding: Operated by Fermi Research Alliance, LLC under Contract No.De-AC02-07CH11359 with the United States Department of Energy. Additional funding provided by Grant Award No. LAB 20-2261, Batavia, IL USA
Fermilab’s Main Injector enclosure houses two accelerators: the Main Injector (MI) and the Recycler (RR). In periods of joint operation, when both machines contain high intensity beam, radiative beam losses from MI and RR overlap on the enclosure’s beam loss monitoring (BLM) system, making it difficult to attribute those losses to a single machine. Incorrect diagnoses result in unnecessary downtime that incurs both financial and experimental cost. In this work, we introduce a novel neural approach for automatically disentangling each machine’s contributions to those measured losses. Using a continuous adaptation of the popular UNet architecture in conjunction with a novel data augmentation scheme, our model accurately infers the machine of origin on a per-BLM basis in periods of joint and independent operation. Crucially, by extracting beam loss information at varying receptive fields, the method is capable of learning both local and global machine signatures and producing high quality inferences using only raw BLM loss measurements.
 
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA28  
About • Received ※ 02 August 2022 — Revised ※ 05 August 2022 — Accepted ※ 06 August 2022 — Issue date ※ 03 September 2022
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WEPA70 Tensor Decomposition for the Compression and Analysis of 10 kHz BPM Data storage-ring, factory, status, monitoring 792
 
  • J. Choi, Y. Hidaka, Y. Hu, G.M. Wang
    BNL, Upton, New York, USA
 
  Funding: This work is supported in part by the U.S. Department of Energy (DOE) under contract No. DE-SC0012704.
In the NSLS-II storage ring during user operation, fast-acquisition (FA) 10-kHz BPM data are collected, and their spectral properties are analyzed. Various periodograms and spectral peaks are being provided every minute, and they are very useful in identifying any changes in the orbit. Unfortunately, because of the large amount of data, only several numbers are being continually archived for later study, and the full raw data are saved only by hand when needed. We are developing methods utilizing tensor decomposition techniques to save and analyze the FA data; this paper reports the current status of this project.
 
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-WEPA70  
About • Received ※ 02 August 2022 — Revised ※ 08 August 2022 — Accepted ※ 10 August 2022 — Issue date ※ 28 September 2022
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