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 memory increases GR-sGB mismatch by more than an order of magnitude.
Constraining superluminal Einstein- Æthergravitythroughgravitationalmemory,
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Binary black hole mergers in Ricci-coupled scalar-Gauss-Bonnet gravity produce an observable scalar breathing memory signal from the merger-driven change in scalar charge, potentially matching the size of the theory's tensor-memory correction.
A framework using scale separation in the Isaacson description defines observable gravitational memory rise for compact binary coalescences, providing a basis for hypothesis testing in LISA data.
Hierarchical Bayesian inference on GWTC-5.0 constrains the memory enhancement factor to 0.26 with large uncertainties consistent with the GR value of 1 and forecasts that 2000 detections are needed for a 1σ constraint away from zero.
Bayesian parameter estimation on simulated LISA data establishes conditions for detecting displacement memory in MBHB events and projects observation rates from population models.
citing papers explorer
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Gravitational Memory from Hairy Binary Black Hole Mergers
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 memory increases GR-sGB mismatch by more than an order of magnitude.
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Scalar memory from compact binary coalescences
Binary black hole mergers in Ricci-coupled scalar-Gauss-Bonnet gravity produce an observable scalar breathing memory signal from the merger-driven change in scalar charge, potentially matching the size of the theory's tensor-memory correction.
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Toward claiming a detection of gravitational memory
A framework using scale separation in the Isaacson description defines observable gravitational memory rise for compact binary coalescences, providing a basis for hypothesis testing in LISA data.
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Constraining Gravitational Wave Memory with Hierarchical Inference
Hierarchical Bayesian inference on GWTC-5.0 constrains the memory enhancement factor to 0.26 with large uncertainties consistent with the GR value of 1 and forecasts that 2000 detections are needed for a 1σ constraint away from zero.
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Detectability of Gravitational-Wave Memory with LISA: A Bayesian Approach
Bayesian parameter estimation on simulated LISA data establishes conditions for detecting displacement memory in MBHB events and projects observation rates from population models.