PIRSA:23040163

Spectrogram correlated stacking: A novel time-frequency domain analysis of the Stochastic Gravitational Wave Background

Afshordi, N. (2023). Spectrogram correlated stacking: A novel time-frequency domain analysis of the Stochastic Gravitational Wave Background. Perimeter Institute. https://pirsa.org/23040163

Afshordi, Niayesh. Spectrogram correlated stacking: A novel time-frequency domain analysis of the Stochastic Gravitational Wave Background. Perimeter Institute, Apr. 24, 2023, https://pirsa.org/23040163 

          @misc{ pirsa_PIRSA:23040163,
            doi = {10.48660/23040163},
            url = {https://pirsa.org/23040163},
            author = {Afshordi, Niayesh},
            keywords = {Cosmology},
            language = {en},
            title = {Spectrogram correlated stacking: A novel time-frequency domain analysis of the Stochastic Gravitational Wave Background},
            publisher = {Perimeter Institute},
            year = {2023},
            month = {apr},
            note = {PIRSA:23040163 see, \url{https://pirsa.org}}
          }

Niayesh Afshordi

University of Waterloo & Perimeter Institute

Talk number
PIRSA:23040163
Collection
Talk Type
Subject
Abstract

The astrophysical stochastic gravitational wave background (SGWB) originates from numerous faint sub-threshold  gravitational wave (GW) signals arising from the coalescing binary compact objects. This background is expected to be discovered from the current (or next-generation) network of GW detectors by cross-correlating the signal between multiple pairs of GW detectors. However, detecting this signal is challenging and the correlation is only detectable at low frequencies due to the arrival time delay between different detectors. In this work, we propose 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 which accounts for the chirping nature of the individual events that comprise SGWB.

We show that SpeCS improves the signal-to-noise for the detection of SGWB by up to an order of magnitude, compared to standard optimal cross-correlation methods which are tuned to measure only the power spectrum of the SGWB signal. SpeCS can probe beyond the power spectrum and its application to the GW data available from the current and next-generation GW detectors would speed up the SGWB discovery. 

based on work with Ramit Dey, Luis Longo, and Suvodip Mukherjee

Zoom link:   https://pitp.zoom.us/j/97091817158?pwd=MHNkdjFQT0plVzJJY2lsOHRxdDdwdz09