The Joint Accelerator Conferences Website (JACoW) is an international collaboration that publishes the proceedings of accelerator conferences held around the world.
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 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 -