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RIS citation export for MOPA28: Semantic Regression for Disentangling Beam Losses in the Fermilab Main Injector and Recycler

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  -