Author: Seiya, K.
Paper Title Page
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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MOPA41 Diagnostics for LINAC Optimization with Machine Learning 139
  • R.V. Sharankova, M.W. Mwaniki, K. Seiya, M.E. Wesley
    Fermilab, Batavia, Illinois, USA
  The Fermilab Linac delivers 400 MeV H beam to the rest of the accelerator chain. Providing stable intensity, energy, and emittance is key since it directly affects downstream machines. To operate high current beam, accelerators must minimize uncontrolled particle loss; this is generally accomplished by minimizing beam emittance. Ambient temperature and humidity variations are known to affect resonance frequency of the accelerating cavities which induces emittance growth. In addition, the energy and phase space distribution of particles emerging from the ion source are subject to fluctuations. To counter these effects we are working on implementing dynamic longitudinal parameter optimization based on Machine Learning (ML). As an input for the ML model, signals from beam diagnostic have to be well understand and reliable. We have been revisiting diagnostics in the linac. In this presentation we discuss the status of the diagnostics and beam studies as well as the status and plans for ML-based optimization.  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA41  
About • Received ※ 05 August 2022 — Accepted ※ 06 August 2022 — Issue date ※ 07 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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WEPA40 The L-CAPE Project at FNAL 719
  • M. Jain, V.C. Amatya, G.U. Panapitiya, J.F. Strube
    PNNL, Richland, Washington, USA
  • B.F. Harrison, K.J. Hazelwood, W. Pellico, B.A. Schupbach, K. Seiya, J.M. St. John
    Fermilab, Batavia, Illinois, USA
  The controls system at FNAL records data asynchronously from several thousand Linac devices at their respective cadences, ranging from 15Hz down to once per minute. In case of downtimes, current operations are mostly reactive, investigating the cause of an outage and labeling it after the fact. However, as one of the most upstream systems at the FNAL accelerator complex, the Linac’s foreknowledge of an impending downtime as well as its duration could prompt downstream systems to go into standby, potentially leading to energy savings. The goals of the Linac Condition Anomaly Prediction of Emergence (L-CAPE) project that started in late 2020 are (1) to apply data-analytic methods to improve the information that is available to operators in the control room, and (2) to use machine learning to automate the labeling of outage types as they occur and discover patterns in the data that could lead to the prediction of outages. We present an overview of the challenges in dealing with time-series data from 2000+ devices, our approach to developing an ML-based automated outage labeling system, and the status of augmenting operations by identifying the most likely devices predicting an outage.  
poster icon Poster WEPA40 [1.870 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-WEPA40  
About • Received ※ 03 August 2022 — Revised ※ 12 August 2022 — Accepted ※ 17 August 2022 — Issue date ※ 31 August 2022
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