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RIS citation export for TUPOA51: First Steps Toward Incorporating Image Based Diagnostics into Particle Accelerator Control Systems Using Convolutional Neural Networks

TY - CONF
AU - Edelen, A.L.
AU - Biedron, S.
AU - Edelen, J.P.
AU - Milton, S.V.
ED - Schaa, Volker RW
ED - Power, Maria
ED - Shiltsev, Vladimir
ED - White, Marion
TI - First Steps Toward Incorporating Image Based Diagnostics into Particle Accelerator Control Systems Using Convolutional Neural Networks
J2 - Proc. of NAPAC2016, Chicago, IL, USA, October 9-14, 2016
C1 - Chicago, IL, USA
T2 - North American Particle Accelerator Conference
T3 - 3
LA - english
AB - At present, a variety of image-based diagnostics are used in particle accelerator systems. Often times, these are viewed by a human operator who then makes appropriate adjustments to the machine. Given recent advances in using convolutional neural networks (CNNs) for image processing, it should be possible to use image diagnostics directly in control routines (NN-based or otherwise). This is especially appealing for non-intercepting diagnostics that could run continuously during beam operation. Here, we show results of a first step toward implementing such a controller: our trained CNN can predict multiple simulated downstream beam parameters at the Fermilab Accelerator Science and Technology (FAST) facility's low energy beamline using simulated virtual cathode laser images, gun phases, and solenoid strengths.
PB - JACoW
CP - Geneva, Switzerland
SP - 390
EP - 393
KW - ion
KW - gun
KW - network
KW - controls
KW - solenoid
DA - 2017/01
PY - 2017
SN - 978-3-95450-180-9
DO - 10.18429/JACoW-NAPAC2016-TUPOA51
UR - https://jacow.org/napac2016/papers/tupoa51.pdf
ER -