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Title Experience with Machine Learning in Accelerator Controls
  • K.A. Brown, S. Binello, T. D'Ottavio, P.S. Dyer, S. Nemesure, D.J. Thomas
    BNL, Upton, Long Island, New York, USA
Abstract The repository of data for the Relativistic Heavy Ion Collider and associated pre-injector accelerators consists of well over half a petabyte of uncompressed data. By todays standard, this is not a large amount of data. However, a large fraction of that data has never been analyzed and likely contains useful information. We will describe in this paper our efforts to use machine learning techniques to pull out new information from existing data. Our focus has been to look at simple problems, such as associating basic statistics on certain data sets and doing predictive analysis on single array data. The tools we have tested include unsupervised learning using Tensorflow, multimode neural networks, hierarchical temporal memory techniques using NuPic, as well as deep learning techniques using Theano and Keras.
Funding Work supported by Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy.
Paper download TUCPA03.PDF [0.935 MB / 7 pages]
Slides download TUCPA03_TALK.PDF [6.658 MB]
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Conference ICALEPCS2017, Barcelona, Spain
Series International Conference on Accelerator and Large Experimental Control Systems (16th)
Proceedings Link to full ICALEPCS2017 Proccedings
Session Data Analytics
Date 10-Oct-17   14:00–15:30
Main Classification Data Analytics
Keywords ion, network, controls, extraction, framework
Publisher JACoW, Geneva, Switzerland
Editors Volker RW Schaa (GSI, Darmstadt, Germany); Isidre Costa (ALBA-CELLS, Cerdanyola del Vallès, Spain); David Fernández (ALBA-CELLS, Cerdanyola del Vallès, Spain); Óscar Matilla (ALBA-CELLS, Cerdanyola del Vallès, Spain)
ISBN 978-3-95450-193-9
Published January 2018
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