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arxiv: 2601.23230 · v3 · pith:KBWYS73Qnew · submitted 2026-01-30 · 🌀 gr-qc

Detectability of Gravitational-Wave Memory with LISA: A Bayesian Approach

Adrien Cogez , Silvia Gasparotto , Jann Zosso , Henri Inchausp\'e , Chantal Pitte , Lorena Maga\~na Zertuche , Antoine Petiteau , Marc Besancon This is my paper

Pith reviewed 2026-05-21 14:19 UTC · model grok-4.3

classification 🌀 gr-qc
keywords gravitational wavesLISAdisplacement memorymassive black hole binariesBayesian inferencegeneral relativity tests
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The pith

LISA can detect and characterize the displacement memory effect from individual massive black hole binary mergers.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines whether the Laser Interferometer Space Antenna can observe the permanent spacetime deformation left by gravitational waves after a massive black hole binary merger. It employs state-of-the-art instrument simulations and Bayesian parameter estimation to determine detectability and to reconstruct the memory amplitude for single events. A sympathetic reader would care because a confirmed detection would enable direct tests of general relativity and competing gravity theories through this distinctive signature. The work also folds in black hole population models to forecast how many such observations might occur during LISA's planned lifetime.

Core claim

Using state-of-the-art simulations of the LISA instrument and Bayesian analysis, the displacement memory effect from individual massive black hole binary merger events is detectable and its amplitude can be reconstructed at a precision that opens the way to tests of general relativity and alternative theories, with estimated observation rates provided by population models.

What carries the argument

Bayesian parameter estimation performed on simulated LISA responses that include the displacement memory contribution from massive black hole binary signals.

If this is right

  • Detection of the memory effect in LISA data would permit quantitative tests of general relativity through its predicted amplitude.
  • The same Bayesian framework can be used to place constraints on alternative gravity theories that modify the memory contribution.
  • Population models allow forecasting of the number of memory detections expected over the LISA mission lifetime.
  • Successful reconstruction of memory amplitude for individual events demonstrates that LISA can extract subtle post-merger features beyond the usual oscillatory waveform.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same analysis approach could be adapted to future ground-based detectors to search for memory in stellar-mass black hole mergers.
  • Non-detection in the first years of LISA operation would tighten the required improvements in waveform modeling or instrument calibration.
  • Combining memory measurements with other general-relativity tests from the same events could provide stronger joint constraints on modified gravity.

Load-bearing premise

The chosen LISA instrument simulations correctly reproduce the detector response to signals that contain the displacement memory effect, and the adopted black hole population models are representative of the actual universe.

What would settle it

A real LISA detection of a massive black hole merger with high signal-to-noise ratio that shows no statistically significant memory offset, or a reconstructed memory amplitude inconsistent with the general-relativity prediction at the level claimed by the analysis.

Figures

Figures reproduced from arXiv: 2601.23230 by Adrien Cogez, Antoine Petiteau, Chantal Pitte, Henri Inchausp\'e, Jann Zosso, Lorena Maga\~na Zertuche, Marc Besancon, Silvia Gasparotto.

Figure 1
Figure 1. Figure 1: FIG. 1. Example of the + polarization of a time-domain waveform with memory effect using the waveform [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FIG. 2. Summarized steps to obtain mock data and tem [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIG. 3. SNR [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: FIG. 4. Memory waterfall plot from the Fig. [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: FIG. 5. log [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: FIG. 7. Mean and dispersion values of ∆ log [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗
Figure 6
Figure 6. Figure 6: FIG. 6. Comparison of the log-likelihood difference for the [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: FIG. 8. Conversion of the SNR waterfall in Fig. [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: FIG. 9. Cropped cornerplots showing parameters estimation of [PITH_FULL_IMAGE:figures/full_fig_p014_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: FIG. 10. Cropped corner plot showing parameters estima [PITH_FULL_IMAGE:figures/full_fig_p014_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: FIG. 11. Measured uncertainties on the amplitude [PITH_FULL_IMAGE:figures/full_fig_p015_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: FIG. 12. Histograms of the number of events such that [PITH_FULL_IMAGE:figures/full_fig_p017_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: FIG. 13. Probability of having a 4-years iteration with [PITH_FULL_IMAGE:figures/full_fig_p017_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: FIG. 14. Time-domain (left) and frequency-domain (right) TDI-A channel obtained, after the response of the links, from the [PITH_FULL_IMAGE:figures/full_fig_p019_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: FIG. 15. Total SNR (left) and SNR of the memory (right) as a function of the total mass [PITH_FULL_IMAGE:figures/full_fig_p020_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: FIG. 16. Total SNR (left) and SNR of the memory (right) depending on the total mass [PITH_FULL_IMAGE:figures/full_fig_p020_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: FIG. 17. SNR of the memory (left) and associated detectability estimation (right) depending on the total mass [PITH_FULL_IMAGE:figures/full_fig_p021_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: FIG. 18. Cornerplot showing parameters estimation using a model with memory (green) and without (red). The values [PITH_FULL_IMAGE:figures/full_fig_p022_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: FIG. 19. Cornerplot showing parameters estimation using a model with memory (green) and without (red). The values [PITH_FULL_IMAGE:figures/full_fig_p023_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: FIG. 20. Comparison between the two components of the complete (2,0) mode. In the time-domain waveform (left), we can [PITH_FULL_IMAGE:figures/full_fig_p024_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: FIG. 21. Cornerplot showing parameters estimation using a model with the (2,2)-mode and the full (2,0)-mode (blue) compared [PITH_FULL_IMAGE:figures/full_fig_p025_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: FIG. 22. Histograms of the number of events such that SNR [PITH_FULL_IMAGE:figures/full_fig_p026_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: FIG. 23. Probability of having an iteration with SNR [PITH_FULL_IMAGE:figures/full_fig_p027_23.png] view at source ↗
read the original abstract

Gravitational wave (GW) astronomy opens a new venue to explore the universe. Future observatories such as LISA, the Laser Interferometer Space Antenna, are expected to observe previously undetectable fundamental physics effects in signals predicted by General Relativity (GR).One particularly interesting such signal is associated to the displacement memory effect, which corresponds to a permanent deformation of spacetime due to the passage of gravitational radiation. In this work, we explore the ability of LISA to observe and characterize this effect. In order to do this, we use state-of-the-art simulations of the LISA instrument, and we perform a Bayesian analysis to assess the detectability and establish general conditions to claim detection of the displacement memory effect from individual massive black hole binary (MBHB) merger events in LISA. We perform parameter estimation both to explore the impact of the displacement memory effect and to reconstruct its amplitude. We discuss the precision at which such a reconstruction can be obtained thus opening the way to tests of GR and alternative theories. To provide astrophysical context, we apply our analysis to black hole binary populations models and estimate the rates at which the displacement memory effect could be observed within the LISA planned lifetime.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The paper examines the detectability and characterization of the gravitational-wave displacement memory effect from individual massive black hole binary (MBHB) mergers using LISA. It employs state-of-the-art LISA instrument simulations combined with Bayesian parameter estimation to assess detectability thresholds, reconstruct memory amplitudes, explore impacts on parameter recovery, and estimate observation rates by applying the analysis to black hole population models over the planned LISA lifetime.

Significance. If the central results hold, the work would establish a concrete pathway for LISA to observe and measure the displacement memory effect, enabling direct tests of general relativity and alternative gravity theories via the permanent spacetime offset. The Bayesian framework for amplitude reconstruction and the use of population models to derive event rates are positive elements that ground the claims in realistic astrophysical contexts.

major comments (1)
  1. [Methods section on LISA instrument simulations and response modeling] The analysis relies on state-of-the-art LISA instrument simulations to model the response to signals containing the displacement memory effect, yet no explicit validation or cross-checks against independent analytical memory waveform models or controlled injections are reported. This is load-bearing for the detectability and reconstruction claims because the memory contribution is a low-frequency, DC-like permanent offset whose accurate capture in the time-delay interferometry channels is essential; any unmodeled attenuation or distortion would directly affect the reported precision and rates.
minor comments (2)
  1. [Abstract] The abstract and introduction would benefit from a brief quantitative summary of the key detectability thresholds or rate estimates to allow readers to assess the strength of the conclusions without reading the full results section.
  2. [Methods] Notation for the memory amplitude parameter and its relation to the waveform model should be clarified with an explicit equation reference early in the methods to avoid ambiguity when discussing reconstruction precision.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their careful reading of the manuscript and for the constructive major comment. We address it point by point below.

read point-by-point responses

  1. Referee: The analysis relies on state-of-the-art LISA instrument simulations to model the response to signals containing the displacement memory effect, yet no explicit validation or cross-checks against independent analytical memory waveform models or controlled injections are reported. This is load-bearing for the detectability and reconstruction claims because the memory contribution is a low-frequency, DC-like permanent offset whose accurate capture in the time-delay interferometry channels is essential; any unmodeled attenuation or distortion would directly affect the reported precision and rates.

    Authors: We appreciate the referee highlighting the need for explicit validation of the LISA response modeling for the displacement memory effect. The instrument simulations are drawn from established LISA Data Challenge pipelines that have been cross-validated for standard waveforms in the literature. To directly address the concern for the memory signal, we have added a dedicated paragraph in the Methods section that reports two new checks: (i) direct comparison of the simulated TDI time series against an independent analytical memory waveform model for the permanent offset, and (ii) controlled injections of memory-only signals into noise-only data to verify unbiased recovery of the DC component without attenuation. These additions confirm that the low-frequency offset is faithfully captured and support the reported detectability thresholds and amplitude reconstruction precision. revision: yes

Circularity Check

0 steps flagged

No significant circularity; claims rest on external simulations and population models

full rationale

The paper performs Bayesian parameter estimation on LISA instrument simulations for MBHB signals including displacement memory, then applies the analysis to external black hole population models to estimate observation rates. No derivation step reduces a prediction to a fitted input by construction, nor does any central claim rest on a self-citation chain or self-definitional ansatz. The workflow is self-contained against external benchmarks and does not exhibit the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review performed on abstract only; no explicit free parameters, axioms, or invented entities are stated in the provided text.

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Forward citations

Cited by 3 Pith papers

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

  1. Gravitational Memory from Hairy Binary Black Hole Mergers

    gr-qc 2026-04 unverdicted novelty 8.0

    Gravitational memory from hairy binary black hole mergers in scalar-Gauss-Bonnet gravity differs from GR by a few percent due to altered nonlinear dynamics, with direct scalar contributions suppressed, and including m...

  2. Scalar memory from compact binary coalescences

    gr-qc 2026-05 conditional novelty 7.0

    In Ricci-coupled scalar-Gauss-Bonnet gravity, the change in scalar charge during binary black hole mergers generates a scalar memory contribution that modifies the total memory signal on observable timescales.

  3. Probing soft signals of gravitational-wave memory with space-based interferometers

    gr-qc 2026-03 conditional novelty 5.0

    Space-based detectors can measure soft displacement-memory signals from gravitational waves at SNR greater than or equal to 10.

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