VAMANA: Modeling Binary Black Hole Population withMinimal Assumptions

The population analysis of compact binaries involves reconstruction of some of the \ac{GW} signal parameters, such as, the mass and the spin distribution, that gave rise to the observed data. In this article, we introduce VAMANA, which reconstructs the binary black-hole population using a mixture model and facilitates excellent fitting of the model with the data. VAMANA uses a mixture of weighted Gaussians to reconstruct the chirp mass distribution. We expect Gaussian mixtures to provide flexibility in modeling complex distributions and enable us in capturing details in the astrophysical chirp mass distribution. Each of the Gaussian in the mixture is combined with another Gaussian and a power-law to simultaneously model the spin component aligned with the orbital angular momentum and the mass-ratio distribution, thus also allowing us to capture their variation with the chirp mass. Additionally, we can also introduce broadband smoothing by restricting the Gaussian mixture to lie within a threshold distance of a predefined reference chirp mass distribution. Using simulated data we show the robustness of our method in reconstructing complex population for a large number of observations. We also apply our method on the publicly available catalog of \ac{GW} observations made during LIGO's and Virgo's first and second observation runs and present the reconstructed mass, spin distribution, and the estimated merger rate of binary black-holes.