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In the post-detection era of gravitational wave (GW) astronomy, core collapse supernovae (CCSN) are one of the most interesting potential sources of signals arriving at the Advanced LIGO detectors. Mukherjee et al. have developed and implemented a new method to search for GW signals from the CCSN search based on a multistage, high accuracy spectral estimation to effectively achieve higher detection signal to noise ratio (SNR). The study has been further enhanced by incorporation of a convolutional neural network (CNN) to significantly reduce false alarm rates (FAR). The combined pipeline is termed multilayer signal estimation (MuLaSE) that works in an integrative manner with the coherent wave burst (cWB) pipeline. In order to compare the performance of this new search pipeline, termed “MuLaSECC”, with the cWB, an extensive analysis has been performed with two families of core collapse supernova waveforms corresponding to two different three dimensional (3D) general relativistic CCSN explosion models, viz. Kuroda 2017 and the Ott 2013. The performance of this pipeline has been characterized through receiver operating characteristics (ROC) and the reconstruction of the detected signals. The MuLaSECC is found to have higher efficiency in low false alarm range, a higher detection probability of weak signals and an improved reconstruction, especially in the lower frequency domain.


© 2021 American Physical Society. Original published version available at

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Physical Review D





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