TY - JOUR
T1 - Bayesian Inference for Gravitational Waves from Binary Neutron Star Mergers in Third Generation Observatories
AU - Smith, Rory
AU - Borhanian, Ssohrab
AU - Sathyaprakash, Bangalore
AU - Hernandez Vivanco, Francisco
AU - Field, Scott E.
AU - Lasky, Paul
AU - Mandel, Ilya
AU - Morisaki, Soichiro
AU - Ottaway, David
AU - Slagmolen, Bram J.J.
AU - Thrane, Eric
AU - Töyrä, Daniel
AU - Vitale, Salvatore
N1 - Publisher Copyright:
© 2021 American Physical Society.
PY - 2021/8/20
Y1 - 2021/8/20
N2 - Third generation (3G) gravitational-wave 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 signal-To-noise ratios and long-duration 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 reduced-order models for ∼90-min-long gravitational-wave signals covering the observing band (5-2048 Hz), speeding up inference by a factor of ∼1.3×104 compared to the calculation times without reduced-order models. The reduced-order models incorporate key physics including the effects of tidal deformability, amplitude modulation due to Earth's rotation, and spin-induced orbital precession. We show how reduced-order modeling can accelerate inference on data containing multiple overlapping gravitational-wave 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 long-lived, overlapping, high signal-To-noise-ratio events present in 3G observatories.
AB - Third generation (3G) gravitational-wave 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 signal-To-noise ratios and long-duration 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 reduced-order models for ∼90-min-long gravitational-wave signals covering the observing band (5-2048 Hz), speeding up inference by a factor of ∼1.3×104 compared to the calculation times without reduced-order models. The reduced-order models incorporate key physics including the effects of tidal deformability, amplitude modulation due to Earth's rotation, and spin-induced orbital precession. We show how reduced-order modeling can accelerate inference on data containing multiple overlapping gravitational-wave 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 long-lived, overlapping, high signal-To-noise-ratio events present in 3G observatories.
UR - http://www.scopus.com/inward/record.url?scp=85113670858&partnerID=8YFLogxK
U2 - 10.1103/PhysRevLett.127.081102
DO - 10.1103/PhysRevLett.127.081102
M3 - Article
SN - 0031-9007
VL - 127
JO - Physical Review Letters
JF - Physical Review Letters
IS - 8
M1 - 081102
ER -