Multiband parameter estimation with phase coherence and extrinsic marginalization: Extracting more information from low-SNR CBC signals in LISA data

TL;DR

This paper introduces a new multiband parameter estimation method for low-SNR binary black hole signals in LISA data, significantly improving detection efficiency and parameter accuracy by marginalizing extrinsic parameters.

Contribution

It presents a novel, efficient joint likelihood approach with extrinsic marginalization, enabling robust analysis of low-SNR multiband gravitational wave sources.

Findings

LISA can contribute nearly double the multiband sources at SNR ~3.

Parameter estimation accuracy surpasses long-duration ET+2CE observations.

First efficient multiband Bayesian analysis on population scale.

Abstract

This paper presents a novel coherent multiband analysis framework for characterizing stellar- and intermediate-mass binary black holes using LISA and next-generation ground-based detectors (ET and CE), leveraging the latest developments in the \texttt{PyCBC} pipeline. Given the population parameters inferred from LVK results and LISA's sensitivity limits at high frequencies, most stellar-mass binary black holes would likely have SNRs below 5 in LISA, but the most state-of-the-art multiband parameter estimation methods, such as those using ET and CE posteriors as priors for LISA, typically struggle to analyze sources with a LISA SNR less than 5. We present a novel coherent multiband parameter estimation method that directly calculates a joint likelihood, which is highly efficient; this efficiency is enabled by multiband marginalization of the extrinsic parameter space, implemented using importance sampling, which can work robustly even when the LISA SNR is as low as 3. Having an SNR of $\sim 3$ allows LISA to contribute nearly double the number of multiband sources. Even if LISA only observes for one year, most of the multiband detector-frame chirp mass's 90\% credible interval (less than $10^{-4} \mathrm{M}_\odot$) is still better than that of the most accurately measured events for ET+2CE network in 7.5 years of observation, by at least one order of magnitude. For the first time, we show efficient multiband Bayesian parameter estimation results on the population scale, which paves the way for large-scale astrophysical tests using multibanding.