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Updated: Oct 21, 2025
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The Generation of Higher-order Laguerre-Gauss Optical Beams for High-precision Interferometry
Published on: August 12, 2013
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Bayesian Inference for Gravitational Waves from Binary Neutron Star Mergers in Third Generation Observatories.
Rory Smith1,2, Ssohrab Borhanian3, Bangalore Sathyaprakash3,4,5
1School of Physics and Astronomy, Monash University, Victoria 3800, Australia.
Physical Review Letters
|September 3, 2021
Summary
Future gravitational-wave detectors will observe many neutron star mergers. Reduced-order models accelerate data analysis, making Bayesian inference computationally feasible for these complex signals.
Area of Science:
- Astrophysics
- Cosmology
- Gravitational-wave astronomy
Background:
- Third generation (3G) gravitational-wave detectors promise high-fidelity observations of coalescing neutron star binaries.
- Analyzing these high signal-to-noise ratio, long-duration signals presents significant computational challenges.
Purpose of the Study:
- To demonstrate the extension of Bayesian inference paradigms for analyzing binary neutron star signals from 3G detectors.
- To assess the computational feasibility of extracting precise scientific data from these future observations.
Main Methods:
- Construction of reduced-order models for gravitational-wave signals spanning 5-2048 Hz.
- Incorporation of key physics into models: tidal deformability, Earth's rotation effects, and spin-induced orbital precession.
- Analysis of computational speedup factors achieved by reduced-order models.
Main Results:
- Reduced-order models accelerate inference by approximately 1.3×10⁴ times compared to standard calculations.
- Demonstrated speedup in analyzing data with multiple overlapping gravitational-wave signals.
- Quantified the relationship between speedup and the number of overlapping signals.
Conclusions:
- Bayesian inference is computationally tractable for analyzing long-lived, overlapping, high signal-to-noise-ratio events expected from 3G observatories.
- Reduced-order modeling is a key technique for enabling precise scientific extraction from future gravitational-wave data.
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