PIRSA:23090103

Video URL

https://pirsa.org/23090103

Advancing Stochastic Gravitational Wave Background Detection with Spectrogram Correlated Stacking (SpeCs)

Dey, R. (2023). Advancing Stochastic Gravitational Wave Background Detection with Spectrogram Correlated Stacking (SpeCs). Perimeter Institute for Theoretical Physics. https://pirsa.org/23090103

Dey, Ramit. Advancing Stochastic Gravitational Wave Background Detection with Spectrogram Correlated Stacking (SpeCs). Perimeter Institute for Theoretical Physics, Sep. 21, 2023, https://pirsa.org/23090103 

          @misc{ scivideos_PIRSA:23090103,
            doi = {10.48660/23090103},
            url = {https://pirsa.org/23090103},
            author = {Dey, Ramit},
            keywords = {Strong Gravity},
            language = {en},
            title = {Advancing Stochastic Gravitational Wave Background Detection with Spectrogram Correlated Stacking (SpeCs)},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2023},
            month = {sep},
            note = {PIRSA:23090103 see, \url{https://scivideos.org/pirsa/23090103}}
          }

Ramit Dey Western University

Talk numberPIRSA:23090103
DOI10.48660/23090103
Source RepositoryPIRSA
Collection
Talk Type Scientific Series
Subject

Abstract

A stochastic gravitational wave background (SGWB) originates from numerous faint gravitational wave (GW) signals arising from coalescing compact binary objects. Based on the current estimated merger rate, the SGWB signal is expected to originate from non-overlapping GW waveforms where the chirping nature of individual events is expected to be preserved. In this talk, we present a novel technique, Spectrogram Correlated Stacking (or SpeCs), which goes beyond the usual cross-correlation (and to higher frequencies) by exploiting the higher-order statistics in the time-frequency domain. This method would account for the chirping nature of the individual events that comprise SGWB and enable us to extract more information from the signal due to its intrinsic non-gaussianity. We show that SpeCs improve the signal-to-noise for the detection of SGWB by a factor close to 8, compared to standard optimal cross-correlation methods which are tuned to measure only the power spectrum of the signal. SpeCs can probe beyond the power spectrum and its application to the GW data available from the current and next-generation detectors would speed up the SGWB discovery.

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Zoom link: https://pitp.zoom.us/j/91002244803?pwd=a0dnMjZEYTEwSHBCVGRSeHB2Y2pJdz09