Fast Bayesian Inference for Long-Duration Gravitational-Wave Signals in 3G detectors

Abstract

Third-generation (3G) gravitational-wave detectors will observe binary-neutron-star inspirals for nearly a day, so Earth's rotation becomes part of the signal rather than a negligible correction. This destroys the usual separation between intrinsic and extrinsic parameters and creates a major computational bottleneck for Bayesian inference. We show that the exact five-harmonic Jaranowski-Krolak-Schutz (JKS) decomposition restores the separation of the rotating antenna amplitude response from the computationally expensive intrinsic waveform calculations. By sampling the intrinsic waveform on a frequency grid set by its phase curvature and then applying error-controlled relative binning, likelihood evaluations for a 21.3-h signal are accelerated by O(10^4), making day-long 3G signal inference practical.