Abstract Details

Name: Sumit Kumar
Affiliation: International centre for theoretical sciences, TIFR, Bengaluru
Conference ID: ASI2017_707
Title : Distinguishing population synthesis models of the evolution of binary black holes using gravitational wave observations
Authors and Co-Authors : Arunava Mukherjee, P.Ajith, Abhiroop Ghosh (ICTS-TIFR) Arnab Dhani (IIT Roorkee) Archisman Ghosh (NIKHEF)
Abstract Type : Poster
Abstract Category : Stars,ISM and the Galaxy
Abstract : Various population synthesis models (Dominik et al 2012) predict the merger rates and distribution of masses and spins for the compact binary objects such as binary black holes (BBH), neutron star-black hole (NSBH) and neutron star-neutron star (NSNS). These population synthesis models are characterized by various unknown parameters related evolution of the compact binary sources such as metalicity, binding energy, maximum mass of neutron star, etc. Advanced LIGO is expected to detect many events for merging compact binaries. In future, we can infer the mass distribution and merger rates from these observations using which we can put statistical constraints on parameter space which defines these models. We present a Bayesian inference method for distinguishing various population synthesis models based on BBH distribution. The Bayesian evidence is calculated for various models and we rank them accordingly. We tested our method on the simulated data by assuming a fiducial model and shown that we can distinguish some of these models using future detection of BBH mergers.