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1 results

  • Background Rejection using Convolutional Neural Networks

  • Adam Zadrożny, Beata Goźlińska
  • Journal:
    Proceedings of the International Astronomical Union / Volume 13 / Issue S338 / October 2017
    Published online by Cambridge University Press:
    29 January 2019, pp. 37-39
    Print publication:
    October 2017
    • Article
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  • The paper presents a proof of concept method of background rejection based on convolutional neural networks (CNN). The method was tested on simulated data and achieved very high accuracy (100%). What is more, method based on CNN is very fast and could be easily applied to wide field surveys. Since early stage results suggest method is very accurate and robust, it could be helpful in creating very low-latency pipelines for EM Follow-up purposes, which will be needed in LIGO-Virgo O3 EM Follow-up.