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Creators/Authors contains: "Massinger, Thomas"

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  1. ; ; (, LIGO Laboratory Summer 2018 Undergraduate Research)
    We develop methods to more efficiently differentiate between gravitational wave signals from binary mergers, and detector noise. We make use of the PyCBC detection pipeline to compile larger amounts of data, including signal and noise, into SNR density plots, and we modified them so that they could be easily interpreted by an image classifier. After selecting the parameters that demonstrated features in the density plots, we created a convolutional neural network to search for these patterns. We trained and tested the neural network over increasingly large and varied data sets. 
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    Accepted Manuscript Full Text Available