Author: Narayanan, A.
Paper Title Page
MOPAB288 Real-Time Edge AI for Distributed Systems (READS): Progress on Beam Loss De-Blending for the Fermilab Main Injector and Recycler 912
 
  • K.J. Hazelwood, M.R. Austin, M.A. Ibrahim, V.P. Nagaslaev, A. Narayanan, D.J. Nicklaus, A.L. Saewert, B.A. Schupbach, K. Seiya, R.M. Thurman-Keup, N.V. Tran
    Fermilab, Batavia, Illinois, USA
  • H. Liu, S. Memik, R. Shi, M. Thieme
    Northwestern University, Evanston, Illinois, USA
  • A. Narayanan
    Northern Illinois University, DeKalb, Illinois, USA
  
  The Fermilab Main Injector enclosure houses two accelerators, the Main Injector and Recycler. During normal operation, high intensity proton beams exist simultaneously in both. The two accelerators share the same beam loss monitors (BLM) and monitoring system. Beam losses in the Main Injector enclosure are monitored for tuning the accelerators and machine protection. Losses are currently attributed to a specific machine based on timing. However, this method alone is insufficient and often inaccurate, resulting in more difficult machine tuning and unnecessary machine downtime. Machine experts can often distinguish the correct source of beam loss. This suggests a machine learning (ML) model may be producible to help de-blend losses between machines. Work is underway as part of the Fermilab Real-time Edge AI for Distributed Systems Project (READS) to develop a ML empowered system that collects streamed BLM data and additional machine readings to infer in real-time, which machine generated beam loss.  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB288  
About • paper received ※ 19 May 2021       paper accepted ※ 29 July 2021       issue date ※ 13 August 2021  
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THPAB243 Optimizing Mu2e Spill Regulation System Algorithms 4281
 
  • A. Narayanan
    Northern Illinois University, DeKalb, Illinois, USA
  • K.J. Hazelwood, M.A. Ibrahim, V.P. Nagaslaev, D.J. Nicklaus, P.S. Prieto, B.A. Schupbach, K. Seiya, R.M. Thurman-Keup, N.V. Tran
    Fermilab, Batavia, Illinois, USA
  • H. Liu, S. Memik, R. Shi, M. Thieme
    Northwestern University, Evanston, Illinois, USA
  
  Funding: The work has been performed at Fermilab. Fermilab is managed by Fermi Research Alliance, LLC (FRA), acting under Contract No. DE-AC02-07CH11359.
A slow extraction system is being developed for the Fermilab’s Delivery Ring to deliver protons to the Mu2e experiment. During the extraction, the beam on target experiences small intensity variations owing to many factors. Various adaptive learning algorithms will be employed for beam regulation to achieve the required spill quality. We discuss here preliminary results of the slow and fast regulation algorithms validation through the computer simulations before their implementation in the FPGA. Particle tracking with sextupole resonance was used to determine the fine shape of the spill profile. Fast semi-analytical simulation schemes and Machine Learning models were used to optimize the fast regulation loop. 		 
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB243  
About • paper received ※ 20 May 2021       paper accepted ※ 28 July 2021       issue date ※ 20 August 2021  
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