Bayesian Inference for Gravitational Waves from Binary Neutron Star Mergers in Third Generation Observatories
Abstract
Third generation (3G) gravitationalwave detectors will observe thousands of coalescing neutron star binaries with unprecedented fidelity. Extracting the highest precision science from these signals is expected to be challenging owing to both high signaltonoise ratios and longduration signals. We demonstrate that current Bayesian inference paradigms can be extended to the analysis of binary neutron star signals without breaking the computational bank. We construct reducedorder models for ∼90 minlong gravitationalwave signals covering the observing band (52048 Hz), speeding up inference by a factor of ∼1.3 ×10^{4} compared to the calculation times without reducedorder models. The reducedorder models incorporate key physics including the effects of tidal deformability, amplitude modulation due to Earth's rotation, and spininduced orbital precession. We show how reducedorder modeling can accelerate inference on data containing multiple overlapping gravitationalwave signals, and determine the speedup as a function of the number of overlapping signals. Thus, we conclude that Bayesian inference is computationally tractable for the longlived, overlapping, high signaltonoiseratio events present in 3G observatories.
 Publication:

Physical Review Letters
 Pub Date:
 August 2021
 DOI:
 10.1103/PhysRevLett.127.081102
 arXiv:
 arXiv:2103.12274
 Bibcode:
 2021PhRvL.127h1102S
 Keywords:

 General Relativity and Quantum Cosmology;
 Astrophysics  High Energy Astrophysical Phenomena;
 Astrophysics  Instrumentation and Methods for Astrophysics
 EPrint:
 9 pages, 3 figures. Published in Physical Review Letters