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Free keywords:
Astrophysics, Instrumentation and Methods for Astrophysics, astro-ph.IM, Astrophysics, High Energy Astrophysical Phenomena, astro-ph.HE,General Relativity and Quantum Cosmology, gr-qc
Abstract:
The next-generation (3G/XG) ground-based gravitational-wave (GW) detectors
such as Einstein Telescope (ET) and Cosmic Explorer (CE) will begin observing
in the next decade. Due to the extremely high sensitivity of these detectors,
the majority of stellar-mass compact-binary mergers in the entire Universe will
be observed. It is also expected that 3G detectors will have significant
sensitivity down to 2-7 Hz; the observed duration of binary neutron star
signals could increase to several hours or days. The abundance and duration of
signals will cause them to overlap in time, which may form a confusion noise
that could affect the detection of individual GW sources when using naive
matched filtering; Matched filtering is only optimal for stationary Gaussian
noise. We create mock data for CE and ET using the latest population models
informed by the GWTC-3 catalog and investigate the performance loss of matched
filtering due to overlapping signals. We find the performance loss mainly comes
from a deviation in the noise's measured amplitude spectral density. The
redshift reach of CE (ET) can be reduced by 15-38 (8-21) % depending on the
merger rate estimate. The direct contribution of confusion noise to the total
SNR is generally negligible compared to the contribution from instrumental
noise. We also find that correlated confusion noise has a negligible effect on
the quadrature summation rule of network SNR for ET, but might reduce the
network SNR of high detector-frame mass signals for detector networks including
CE if no mitigation is applied. For ET, the null stream can mitigate the
astrophysical foreground. For CE, we demonstrate that a computationally
efficient, straightforward single-detector signal subtraction method suppresses
the total noise to almost the instrument noise level; this will allow for
near-optimal searches.
