Bayesian parameter estimation of stellar-mass black-hole binaries with LISA
Riccardo Buscicchio, Antoine Klein, Elinore Roebber, Christopher J., Moore, Davide Gerosa, Eliot Finch, Alberto Vecchio

TL;DR
This paper develops a Bayesian analysis pipeline for measuring properties of stellar-mass black hole binaries with LISA, demonstrating high-precision parameter estimation on simulated data including eccentric and precessing systems.
Contribution
It introduces an efficient Bayesian parameter-estimation method using nested sampling and accurate waveforms for LISA data analysis of black hole binaries.
Findings
Successfully recovered 22 binaries with SNR > 8 from simulated data.
Achieved mass measurements with 0.02 solar mass accuracy at 90% confidence.
Accurately estimated sky location and eccentricity for specific sources.
Abstract
We present a Bayesian parameter-estimation pipeline to measure the properties of inspiralling stellar-mass black hole binaries with LISA. Our strategy (i) is based on the coherent analysis of the three noise-orthogonal LISA data streams, (ii) employs accurate and computationally efficient post-Newtonian waveforms accounting for both spin-precession and orbital eccentricity, and (iii) relies on a nested sampling algorithm for the computation of model evidences and posterior probability density functions of the full 17 parameters describing a binary. We demonstrate the performance of this approach by analyzing the LISA Data Challenge (LDC-1) dataset, consisting of 66 quasi-circular, spin-aligned binaries with signal-to-noise ratios ranging from 3 to 14 and times to merger ranging from 3000 to 2 years. We recover 22 binaries with signal-to-noise ratio higher than 8. Their chirp masses are…
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