Bajes, Bayesian inference of multimessenger transients
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We present Bajes, a parallel and lightweight framework for Bayesian inference of multimessenger transients based on Markov-chain–Monte-Carlo and nested sampling algorithms. We perform multimessenger inference on the binary neutron star merger GW170817 and its electromagnetic counterpart AT2017gfo. Mapping the ejecta properties resorting to fit formulae calibrated on targeted numerical relativity simulations, it is possible to constrain the measurement of the reduced tidal parameter to Λ = 430±160 at the 90% confidence level. This information can be traslated in terms of the neutron star equation of state, predicting a radius of an irrotational neutron star of 1.4 M⊙ of 11.99±0.84 km. Furthermore, we employ the gravitational-wave pipeline in the study of binary neutron star postmerger injections with 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.
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