bilby in space: Bayesian inference for transient gravitational-wave signals observed with LISA

Abstract

ABSTRACT The Laser Interferometer Space Antenna (LISA) is scheduled to launch in the mid-2030s, and is expected to observe gravitational-wave candidates from massive black hole binary mergers, extreme mass ratio inspirals, and more. Accurately inferring the source properties from the observed gravitational-wave signals is crucial to maximize the scientific return of the LISA mission. bilby, the user-friendly Bayesian inference library, is regularly used for performing gravitational-wave inference on data from existing ground-based gravitational-wave detectors. Given that Bayesian inference with LISA includes additional subtitles and complexities beyond its ground-based counterpart, in this work we introduce bilby_lisa , a python package that extends bilby to perform parameter estimation with LISA. We show that full nested sampling can be performed to accurately infer the properties of LISA sources from transient gravitational-wave signals in (a) zero noise and (b) idealized instrumental noise. By focusing on massive black hole binary mergers, we demonstrate that higher order multipole waveform models can be used to analyse a year’s worth of simulated LISA data, and discuss the computational cost and performance of full nested sampling compared with techniques for optimizing likelihood calculations, such as the heterodyned likelihood.