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Forecasting the detection capabilities of third-generation gravitational-wave detectors using texttt{GWFAST}

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arxiv 2207.02771 v3 pith:U2J6PSNO submitted 2022-07-06 gr-qc astro-ph.COastro-ph.HE

Forecasting the detection capabilities of third-generation gravitational-wave detectors using texttt{GWFAST}

classification gr-qc astro-ph.COastro-ph.HE
keywords textttgravitational-wavegwfastcodedetectorsdistributionneutronaccuracy
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We introduce $\texttt{GWFAST}$, a novel Fisher-matrix code for gravitational-wave studies, tuned toward third-generation gravitational-wave detectors such as Einstein Telescope (ET) and Cosmic Explorer (CE). We use it to perform a comprehensive study of the capabilities of ET alone, and of a network made by ET and two CE detectors, as well as to provide forecasts for the forthcoming O4 run of the LVK collaboration. We consider binary neutron stars, binary black holes and neutron star-black hole binaries, and compute basic metrics such as the distribution of signal-to-noise ratio (SNR), the accuracy in the reconstruction of various parameters (including distance, sky localization, masses, spins and, for neutron stars, tidal deformabilities), and the redshift distribution of the detections for different thresholds in SNR and different levels of accuracy in localization and distance measurement. We examine the expected distribution and properties of `golden events', with especially large values of the SNR. We also pay special attention to the dependence of the results on astrophysical uncertainties and on various technical details (such as choice of waveforms, or the threshold in SNR), and we compare with other Fisher codes in the literature. In a companion paper we discuss the technical aspects of the code. Together with this paper, we publicly release the code $\texttt{GWFAST}$ at https://github.com/CosmoStatGW/gwfast, and the library $\texttt{WF4Py}$ implementing state-of-the-art gravitational-wave waveforms in pure $\texttt{Python}$ at https://github.com/CosmoStatGW/WF4Py.

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Cited by 11 Pith papers

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