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- W2969277515 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." @default.
- W2969277515 created "2019-08-29" @default.
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- W2969277515 date "2019-08-24" @default.
- W2969277515 modified "2023-09-23" @default.
- W2969277515 title "Characterizing High Level LIGO Gravitational Wave Data Using Deep Learning" @default.
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- W2969277515 doi "https://doi.org/10.1007/978-3-030-29516-5_64" @default.
- W2969277515 hasPublicationYear "2019" @default.
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