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

Connor R. Weaving*, Laura K. Nuttall, Ian W. Harry, Shichao Wu, Alexander Nitz

*Corresponding author for this work

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Abstract

The laser interferometer space antenna (LISA), due for launch in the mid 2030s, is expected to observe gravitational waves (GWs) from merging massive black hole binaries (MBHBs). 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 (SNRs) 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’s Challenge 2a (‘Sangria’), which contains MBHBs and thousands of galactic binaries (GBs) in simulated instrumental LISA noise. Our search identifies all six 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 eight 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.

Original languageEnglish
Article number025006
Number of pages25
JournalClassical and Quantum Gravity
Volume41
Issue number2
Early online date27 Dec 2023
DOIs
Publication statusPublished - 18 Jan 2024

Keywords

  • data analysis
  • gravitational waves
  • LISA
  • massive black hole binaries
  • PyCBC
  • UKRI
  • MRC
  • MR/T01881X/1
  • STFC
  • ST/X002225/1

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