Bayesian inference of multimessenger astrophysical data: Methods and applications to gravitational waves

We present BAJES, a parallel and lightweight framework for Bayesian inference of multimessenger transients. BAJES is a PYTHON modular package with minimal dependencies on external libraries adaptable to the majority of the Bayesian models and to various sampling methods. We describe the general workflow and the parameter estimation pipeline for compact-binary-coalescence gravitational-wave transients. The latter is validated against injections of binary black hole and binary neutron star waveforms, including confidence interval tests that demonstrate the inference is well calibrated. Binary neutron star postmerger injections are also studied using a network of five detectors made of LIGO, Virgo, KAGRA, and Einstein Telescope. Postmerger signals will be detectable for sources at ≲80 Mpc , with Einstein Telescope contributing over 90% of the total signal-to-noise ratio. As a full scale application, we reanalyze the gravitational-wave transients catalog-1 black hole transients using the effective-one-body TEOBResumS approximant and reproduce selected results with other approximants. BAJES inferences are consistent with previous results; the direct comparison of BAJES and BILBY analyses of GW150914 shows a maximum Jensen-Shannon divergence of 5.2 ×10^-4 . GW170817 is reanalyzed using TaylorF2 with 5.5PN point mass and 7.5PN tides, TEOBResumSPA, and IMPRhenomPv2_NRTidal with different cutoff frequencies of 1024 and 2048 Hz. We find that the former choice minimizes systematics on the reduced tidal parameter, while a larger amount of tidal information is gained with the latter choice. BAJES can perform these analyses in about 1 day using 128 CPUs.