Adapting the PyCBC pipeline to find and infer the properties of gravitational waves from massive black hole binaries in LISA

TL;DR

This paper adapts the PyCBC pipeline for detecting and analyzing gravitational waves from massive black hole binaries in LISA data, demonstrating successful identification and parameter estimation in simulated challenges.

Contribution

It introduces a modified PyCBC pipeline capable of searching for and inferring properties of MBHBs in LISA data, tested on simulated datasets with promising results.

Findings

Successfully identified all 6 MBHB signals with over 92% of optimal SNR.

Recovered masses and spins within 90% confidence intervals.

Observed biases in sky position recovery and effects of galactic binaries on parameter estimation.

Abstract

The Laser Interferometer Space Antenna (LISA), due for launch in the mid 2030s, is expected to observe gravitational waves (GW)s from merging massive black hole binaries (MBHB)s. These signals can last from days to months, depending on the masses of the black holes, and are expected to be observed with high signal to noise ratios (SNR)s out to high redshifts. We have adapted the PyCBC software package to enable a template bank search and inference of GWs from MBHBs. The pipeline is tested on the LISA data challenge (LDC)'s Challenge 2a (\enquote{Sangria}), which contains MBHBs and thousands of galactic binaries (GBs) in simulated instrumental LISA noise. Our search identifies all 6 MBHB signals with more than $92\%$ of the optimal SNR. The subsequent parameter inference step recovers the masses and spins within their $90\%$ confidence interval. Sky position parameters have 8 high likelihood modes which are recovered but often our posteriors favour the incorrect sky mode. We observe that the addition of GBs biases the parameter recovery of masses and spins away from the injected values, reinforcing the need for a global fit pipeline which will simultaneously fit the parameters of the GB signals before estimating the parameters of MBHBs.