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Detecting the gravitational wave memory effect with TianQin

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arxiv 2207.13009 v2 pith:ETFFSQDZ submitted 2022-07-26 gr-qc hep-th

Shuo Sun , Changfu Shi , Jian-dong Zhang , Jianwei Mei This is my paper · ORCID
classification gr-qchep-th
keywords memoryeffectgravitationalwavetianqinchancesignalsdetect
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abstract

The gravitational wave memory effect is a prediction of general relativity. The presence of memory effect in gravitational wave signals not only provides the chance to test an important aspect of general relativity, but also represents a potentially non-negligible contribution to the waveform for certain gravitational wave events. In this paper, we study the prospect of detecting the gravitational wave memory effect directly with the planned space-based gravitational wave detector -- TianQin. We find that during its 5 years operation, for the gravitational wave signals that could be detected by TianQin, about $0.5\sim2.0$ signals may contain displacement memory effect with signal-to-noise ratios (SNRs) greater than 3. This suggests that the chance for TianQin to detect the displacement memory effect directly is low but not fully negligible. In contrast, the chance to detect the spin memory is negligible. We also study that in which parameter space, the memory effect is expected to be significant in waveform modeling.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Gravitational lensing of gravitational waves: universal characteristics of strongly lensed memory waveforms

    gr-qc 2025-10 conditional novelty 7.0 of 10

    Strongly lensed gravitational-wave memory waveforms acquire universal parity signatures—odd for type I/III images, even for type II—that can identify image type via a simple step-function approximation.

  2. Identifying Microlensing by Compact Dark Matter through Diffraction Patterns in Gravitational Waves with Machine Learning

    astro-ph.IM 2025-09 conditional novelty 6.0 of 10

    A wavelet-convolution neural network distinguishes simulated lensed from unlensed gravitational waves with 92.2% accuracy (AUC 0.965) using wave-optics diffraction patterns.

  3. Flyby-induced displacement: analytic solution

    gr-qc 2025-02 accept novelty 5.0 of 10

    The authors derive exact analytic solutions for gravitational wave displacement memory when the flyby profile is approximated by the hyperbolic Scarf potential, with magic amplitude values |g|=(2n+1) sqrt(n(n+1)).

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