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Constraining the stochastic gravitational wave background with photometric surveys

Wang, Yijun and Pardo, Kris and Chang, Tzu-Ching and Dorè, Olivier (2022) Constraining the stochastic gravitational wave background with photometric surveys. Physical Review D, 106 (8). Art. No. 084006. ISSN 2470-0010. doi:10.1103/physrevd.106.084006.

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The detection of the stochastic gravitational wave background (SGWB) is essential for understanding black hole populations, especially for supermassive black hole binaries. The recent promising results from various pulsar timing array (PTA) collaborations allude to an imminent detection. In this paper, we investigate the relative astrometric gravitational wave detection method, which can contribute to SGWB studies in the microhertz range. We consider the Roman Space Telescope and Gaia as candidates and quantitatively discuss the survey sensitivity in both the frequency and spatial domains. We emphasize the importance of survey specific constraints on performance estimates by considering mean field of view (FoV) signal subtraction and angular power spectrum binning. We conclude that if the SGWB is at a similar level as in PTA estimates, both Roman and Gaia have the potential to detect this frequency-domain power excess. However, both Roman and Gaia are subject to FoV limitations, and are unlikely to be sensitive to the spatial pattern of the SGWB.

Item Type:Article
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URLURL TypeDescription ItemDiscussion Paper
Wang, Yijun0000-0002-5581-2001
Pardo, Kris0000-0002-9910-6782
Chang, Tzu-Ching0000-0001-5929-4187
Dorè, Olivier0000-0001-7432-2932
Additional Information:We thank the anonymous referees for helpful comments. We thank Andrew Casey-Clyde for helpful discussions. Part of this work was done at Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. This work was supported by NASA Grant No. 15-WFIRST15-0008 Cosmology with the High Latitude Survey Roman Science Investigation Team (SIT). Software: astropy, matplotlib, and numpy.
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Issue or Number:8
Record Number:CaltechAUTHORS:20221114-804506000.3
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:117853
Deposited By: Research Services Depository
Deposited On:29 Nov 2022 17:43
Last Modified:29 Nov 2022 17:43

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