VAMANA: modelling binary black hole population with minimal assumptions

Tiwari, Vaibhav 2021. VAMANA: modelling binary black hole population with minimal assumptions. Classical and Quantum Gravity 38 (15) , 155007. 10.1088/1361-6382/ac0b54

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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 wing 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 populations for a large number of observations. We also apply our method to the publicly available catalog of 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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Physics and Astronomy
Additional Information:	Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence
Publisher:	IOP Publishing
ISSN:	0264-9381
Date of First Compliant Deposit:	2 July 2021
Date of Acceptance:	15 June 2021
Last Modified:	05 Jan 2024 05:17
URI:	https://orca.cardiff.ac.uk/id/eprint/142329

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