| Title |
Deep Learning Applied for Multi-Slit Imaging Based Beam Size Monitor |
| Authors |
- B. Gao, Y.B. Lengpresenter
SSRF, Shanghai, People’s Republic of China
- X.Y. Xu
SINAP, Shanghai, People’s Republic of China
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| Abstract |
In order to satisfy the requirement of high speed measurement and improve the accuracy of BSM (beam size monitor), multi-slit imaging based BSM has been proposed by SSRF at 2017. However, it is very difficult to deconvolve the image and figure out the beam size, which requires dedicated algorithms to solve this issue. Deep learning is one of the most popular algorithms, which can learn to mimic any distribution of data. In the region of Beam instrumentation, they can be taught to deal with many difficult problem. In this paper, multi-layer neural network is used to process the images from the multi-slit imaging system. Training processes, struct of the neural networks and the result of the experiments will be presented.
|
| Paper |
download WEPGW049.PDF [0.518 MB / 4 pages] |
| Export |
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| Conference |
IPAC2019 |
| Series |
International Particle Accelerator Conference (10th) |
| Location |
Melbourne, Australia |
| Date |
19-24 May 2019 |
| Publisher |
JACoW Publishing, Geneva, Switzerland |
| Editorial Board |
Mark Boland (UoM, Saskatoon, SK, Canada); Hitoshi Tanaka (KEK, Tsukuba, Japan); David Button (ANSTO, Kirrawee, NSW, Australia); Rohan Dowd (ANSTO, Kirrawee, NSW, Australia); Volker RW Schaa (GSI, Darmstadt, Germany); Eugene Tan (ANSTO, Kirrawee, NSW, Australia) |
| Online ISBN |
978-3-95450-208-0 |
| Received |
15 May 2019 |
| Accepted |
21 May 2019 |
| Issue Date |
21 June 2019 |
| DOI |
doi:10.18429/JACoW-IPAC2019-WEPGW049 |
| Pages |
2587-2590 |
| Copyright |
 Published by JACoW Publishing under the terms of the Creative Commons Attribution 3.0 International license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI. |
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