VAMANA: Modeling Binary Black Hole Population withMinimal Assumptions

Abstract

We introduce VAMANA, that models the binary black-hole population using a mixture model and facilitates excellent fitting of the model with the data. Flexibility of our modeler results in smaller uncertainties on the posterior distributions and the estimated merger rates allowing extraction of features in the population that may not be visible in parametric methods that model the population using phenomenological models. We present the mass and the spin distribution modeled on the binary black-hole mergers observed during LIGO's and Virgo's first and second observation runs and estimate the binary black-hole merger rate to be 26.613.8+18.2Gpc3yr126.6^{+18.2}_ {-13.8}\,\mathrm{Gpc}^{-3}\mathrm{yr}^{-1}

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