Journal
SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY
Volume 65, Issue 10, Pages -Publisher
SCIENCE PRESS
DOI: 10.1007/s11433-022-1930-9
Keywords
dark matter; gravitational wave; black hole
Categories
Funding
- National Key Research and Development Program of China [2020YFC2201501]
- National Natural Science Foundation of China (NSFC) [11851302]
- Fundamental Research Funds for the Central Universities
- Key Research Program of the Chinese Academy ofSciences [XDPB15]
- NSFC [11851302, 11851303, 11690022, 11747601]
- Strategic Priority Research Program of the Chinese Academy of Sciences [XDB23030100]
- NSFC Special Fundfor Theoretical Physics [12147103]
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The exact properties of dark matter remain largely unknown despite the accumulating evidence. This study finds that dark matter can have significant effects on the characteristic strain of gravitational waves, particularly at low and high frequencies. These effects become more pronounced as the density of dark matter increases.
The exact properties of dark matter remain largely unknown despite the accumulating evidence. If dark matter is composed of weakly interacting massive particles, it would be accreted by the black hole in the galactic center and form a dense, cuspy spike. Dynamical friction from this spike may have observable effects in a binary system. We consider extreme-mass-ratio inspiral (EMRI) binaries comprising massive black holes harbored in dark matter spikes and stellar mass objects in elliptic orbits. We find that the gravitational-wave waveforms in the frequency domain can be substantially modified. In particular, we show that dark matter can suppress the characteristic strain of a gravitational wave at low frequency but enhance it at a higher domain. These effects are more dramatic as the dark matter density increases. The results indicate that the signal-to-noise ratio of EMRIs can be strongly reduced near 10(-3)-0.3 Hz but enhanced near 1.0 Hz with a higher sensitivity, which can be probed via the future space-borne gravitational-wave (GW) detectors, LISA and TAIJI. The findings will have important impacts on the detection and parameter inference of EMRIs.
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