TY - CONF AU - Berlioz, J.R. AU - Arnold, J.M.S. AU - Austin, M.R. AU - Hanlet, P.M. AU - Hazelwood, K.J. AU - Ibrahim, M.A. AU - Jiang, J. AU - Liu, H. AU - Memik, S. AU - Narayanan, A. AU - Nicklaus, D.J. AU - Pradhan, G. AU - Saewert, A.L. AU - Schupbach, B.A. AU - Shi, R. AU - Thieme, M. AU - Thurman-Keup, R.M. AU - Tran, N.V. AU - Ulusel, D. ED - Biedron, Sandra ED - Simakov, Evgenya ED - Milton, Stephen ED - Anisimov, Petr M. ED - Schaa, Volker R.W. TI - Synchronous High-Frequency Distributed Readout for Edge Processing at the Fermilab Main Injector and Recycler J2 - Proc. of NAPAC2022, Albuquerque, NM, USA, 07-12 August 2022 CY - Albuquerque, NM, USA T2 - International Particle Accelerator Conference T3 - 5 LA - english AB - 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. PB - JACoW Publishing CP - Geneva, Switzerland SP - 79 EP - 82 KW - distributed KW - controls KW - real-time KW - Ethernet KW - operation DA - 2022/10 PY - 2022 SN - 2673-7000 SN - 978-3-95450-232-5 DO - doi:10.18429/JACoW-NAPAC2022-MOPA15 UR - https://jacow.org/napac2022/papers/mopa15.pdf ER - TY - CONF AU - Thieme, M. AU - Arnold, J.M.S. AU - Austin, M.R. AU - Hanlet, P.M. AU - Hazelwood, K.J. AU - Ibrahim, M.A. AU - Liu, H. AU - Memik, S. AU - Nagaslaev, V.P. AU - Narayanan, A. AU - Nicklaus, D.J. AU - Pradhan, G. AU - Saewert, A.L. AU - Schupbach, B.A. AU - Seiya, K. AU - Shi, R. AU - Thurman-Keup, R.M. AU - Tran, N.V. ED - Biedron, Sandra ED - Simakov, Evgenya ED - Milton, Stephen ED - Anisimov, Petr M. ED - Schaa, Volker R.W. TI - Semantic Regression for Disentangling Beam Losses in the Fermilab Main Injector and Recycler J2 - Proc. of NAPAC2022, Albuquerque, NM, USA, 07-12 August 2022 CY - Albuquerque, NM, USA T2 - International Particle Accelerator Conference T3 - 5 LA - english AB - 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. PB - JACoW Publishing CP - Geneva, Switzerland SP - 112 EP - 115 KW - operation KW - real-time KW - distributed KW - proton KW - beam-losses DA - 2022/10 PY - 2022 SN - 2673-7000 SN - 978-3-95450-232-5 DO - doi:10.18429/JACoW-NAPAC2022-MOPA28 UR - https://jacow.org/napac2022/papers/mopa28.pdf ER - TY - CONF AU - Narayanan, A. AU - Arnold, J.M.S. AU - Austin, M.R. AU - Berlioz, J.R. AU - Hanlet, P.M. AU - Hazelwood, K.J. AU - Ibrahim, M.A. AU - Jiang, J. AU - Liu, H. AU - Memik, S. AU - Nagaslaev, V.P. AU - Nicklaus, D.J. AU - Pradhan, G. AU - Prieto, P.S. AU - Saewert, A.L. AU - Schupbach, B.A. AU - Seiya, K. AU - Shi, R. AU - Thieme, M. AU - Thurman-Keup, R.M. AU - Tran, N.V. AU - Ulusel, D. ED - Biedron, Sandra ED - Simakov, Evgenya ED - Milton, Stephen ED - Anisimov, Petr M. ED - Schaa, Volker R.W. TI - Machine Learning for Slow Spill Regulation in the Fermilab Delivery Ring for Mu2e J2 - Proc. of NAPAC2022, Albuquerque, NM, USA, 07-12 August 2022 CY - Albuquerque, NM, USA T2 - International Particle Accelerator Conference T3 - 5 LA - english AB - 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. PB - JACoW Publishing CP - Geneva, Switzerland SP - 214 EP - 217 KW - controls KW - extraction KW - quadrupole KW - experiment KW - target DA - 2022/10 PY - 2022 SN - 2673-7000 SN - 978-3-95450-232-5 DO - doi:10.18429/JACoW-NAPAC2022-MOPA75 UR - https://jacow.org/napac2022/papers/mopa75.pdf ER -