Author: Shi, R.
Paper Title Page
MOPA15 Synchronous High-Frequency Distributed Readout for Edge Processing at the Fermilab Main Injector and Recycler 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 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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MOPA75 Machine Learning for Slow Spill Regulation in the Fermilab Delivery Ring for Mu2e 214
 
  • A. Narayanan
    Northern Illinois University, DeKalb, Illinois, USA
  • J.M.S. Arnold, M.R. Austin, J.R. Berlioz, P.M. Hanlet, K.J. Hazelwood, M.A. Ibrahim, V.P. Nagaslaev, D.J. Nicklaus, G. Pradhan, P.S. Prieto, A.L. Saewert, B.A. Schupbach, K. Seiya, 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
 
  Funding: Work done partly (READS) collaboration at Fermilab (Grant Award No. LAB 20-2261). Fermilab is managed by Fermi Research Alliance, LLC (FRA), acting under Contract No. DE-AC02-07CH11359.
A third-integer resonant slow extraction system is being developed for the Fermilab’s Delivery Ring to deliver protons to the Mu2e experiment. During a slow extraction process, the beam on target is liable to experience small intensity variations due to many factors. Owing to the experiment’s strict requirements in the quality of the spill, a Spill Regulation System (SRS) is currently under design. The SRS primarily consists of three components - slow regulation, fast regulation, and harmonic content tracker. In this presentation, we shall present the investigations of using Machine Learning (ML) in the fast regulation system, including further optimizations of PID controller gains for the fast regulation, prospects of an ML agent completely replacing the PID controller using supervised learning schemes such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) ML models, the simulated impact and limitation of machine response characteristics on the effectiveness of both PID and ML regulation of the spill. We also present here nascent results of Reinforcement Learning efforts, including continuous-action soft actor-critic methods, to regulate the spill rate.
 
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA75  
About • Received ※ 03 August 2022 — Revised ※ 08 August 2022 — Accepted ※ 18 September 2022 — Issue date ※ 05 October 2022
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