VAMANA: Modeling Binary Black Hole Population with Minimal Assumptions
Vaibhav Tiwari

TL;DR
VAMANA is a flexible mixture model approach for reconstructing the complex population distributions of binary black holes from gravitational wave data, capturing mass and spin variations with minimal assumptions.
Contribution
It introduces a novel mixture model framework that accurately reconstructs binary black hole populations, including mass and spin distributions, from gravitational wave observations.
Findings
Successfully reconstructs complex mass and spin distributions.
Demonstrates robustness with simulated data and real GW observations.
Provides estimates of merger rates for binary black holes.
Abstract
The population analysis of compact binaries involves the reconstruction of some of the gravitational wave (GW) signal parameters, such as, the mass and the spin distribution, that gave rise to the observed data. This article introduces VAMANA, which reconstructs the binary black hole population using a mixture model and facilitates excellent density measurement as informed by 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…
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