Abstract
This study aims to build a CNN based classifier for characterizing LIGO gravitational wave data for transient noise analysis. The databank was generated from LIGO’s open source archives and a model was created to flag commonly occurring erroneous noise waveforms. The model developed was able to categorize incoming noise with an accuracy of >99% and also gave insights into preprocessing techniques that can be performed for best performance. This study may be extended to various fields of applied science where data size is colossal and interpretation is usually tedious and often ambiguous.
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Acknowledgment
This research has made use of data, software and/or web tools obtained from the Gravitational Wave Open Science Center (https://www.gw-openscience.org), a service of LIGO Laboratory, the LIGO Scientific Collaboration and the Virgo Collaboration. LIGO is funded by the U.S. National Science Foundation. Virgo is funded by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale della Fisica Nucleare (INFN) and the Dutch Nikhef, with contributions by Polish and Hungarian institutes.
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Goel, L., Mukherjee, J. (2020). Characterizing High Level LIGO Gravitational Wave Data Using Deep Learning. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1037. Springer, Cham. https://doi.org/10.1007/978-3-030-29516-5_64
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