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- W2902369498 abstract "The detection of gravitational waves opened a new window on the cosmos. The Advanced LIGO and Advanced Virgo interferometers will probe a larger volume of Universe and discover new gravitational wave emitters. Characterizing these detectors is of primary importance in order to recognize the main sources of noise and optimize the sensitivity of the searches. Glitches are transient noise events that can impact the data quality of the interferometers and their classification is an important task for detector characterization. In this paper we present a classification method for short transient signals based on a Wavelet decomposition and de-noising and a classification of the extracted features based on XGBoost algorithm. Although the results show the accuracy is lower than that obtained with the use of deep learning, this method which extracts features while detecting signals in real time, can be configured as a fast classification system." @default.
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- W2902369498 date "2018-09-01" @default.
- W2902369498 modified "2023-09-24" @default.
- W2902369498 title "Wavelet-Based Classification of Transient Signals for Gravitational Wave Detectors" @default.
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- W2902369498 doi "https://doi.org/10.23919/eusipco.2018.8553393" @default.
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