Multi-messenger Analysis of Supermassive Black Hole Binaries: The Joint-likelihood Approach

Abstract Supermassive black hole binaries (SMBHBs) formed in galaxy mergers are promising multi-messenger sources. They can be identified as quasars with periodic variability in electromagnetic (EM) time-domain surveys. The most massive of those systems can be detected by Pulsar Timing Arrays (PTAs) in the nanohertz frequency gravitional-wave (GW) band. We present a method to simultaneously analyze EM lightcurves and PTA observations as a multi-messenger data stream. For this, we employ a joint likelihood analysis in which the likelihood of the EM data and the PTA likelihood are multiplied. We test this approach by simulating 208 binary signals that can be detected both by the Rubin Observatory in the nominal ten-year survey and by a PTA dataset with a ~30-year baseline, which resembles our expectations for a dataset of the International Pulsar Timing Array (IPTA) collaboration in ~2035. We compare our multi-messenger analysis with analyses that take into account the EM and PTA data separately. We find that the joint likelihood approach results in improved parameter estimation with smaller percent errors compared to the distinct analyses that consider only EM or PTA data separately. Among the SMBHB parameters, the binary total mass and the orbital inclination show the greatest improvement. We also compare our multi-messenger pipeline with an analysis, in which the EM constraints are used as priors to the PTA analysis. We demonstrate that the joint likelihood approach delivers tighter constraints on all binary parameters, with systematically higher values of Kullback–Leibler divergence, which measures the deviation of the posterior distribution from the prior.