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Published on: August 12, 2013
Inferring Fundamental Spacetime Symmetries with Gravitational-Wave Memory: From LISA to the Einstein Telescope.
Boris Goncharov1,2,3,4, Laura Donnay5,6, Jan Harms1,2
1<a href="https://ror.org/043qcb444">Gran Sasso Science Institute (GSSI)</a>, I-67100 L'Aquila, Italy.
Gravitational wave (GW) memory offers new ways to measure spacetime symmetries. The Einstein Telescope can precisely constrain displacement and spin memory, improving binary black hole merger parameter estimations.
Area of Science:
- Astrophysics
- General Relativity
- Gravitational Wave Astronomy
Background:
- Gravitational wave (GW) memory, a subtle but persistent effect in spacetime, has traditionally been a challenging signal to detect.
- Its potential for probing fundamental physics beyond standard GW searches has remained largely unexplored.
- Understanding GW memory is crucial for advancing our knowledge of gravity and the universe.
Purpose of the Study:
- To establish gravitational wave memory as a tool for measuring spacetime symmetries.
- To investigate the potential of displacement and spin memory for probing these symmetries.
- To assess the impact of neglecting memory effects on binary black hole merger parameter estimations.
Main Methods:
- Theoretical analysis of gravitational wave memory effects.
- Simulations incorporating the Einstein Telescope's (ET) projected sensitivity.
- Quantification of measurement uncertainties for binary black hole merger parameters.
Main Results:
- Displacement and spin memory can be used to probe spacetime symmetries.
- The Einstein Telescope can constrain displacement memory strain amplitude to 2% and spin memory to 22%.
- Neglecting memory effects can lead to ~10% overestimation of BBH merger parameter uncertainties in ET.
Conclusions:
- Gravitational wave memory is a key observable for measuring spacetime symmetries.
- The Einstein Telescope will provide unprecedented precision in constraining memory effects.
- Accurate modeling of GW memory is essential for precise astrophysical parameter estimation.
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