Calibrating spectral siren cosmology with synthetic catalogs of binary black hole mergers
Arianna Scarpa, Simone Mastrogiovanni, Filippo Santoliquido, Manuel Arca-Sedda

TL;DR
This paper develops a Normalizing Flow-based model trained on synthetic binary black hole catalogs to calibrate spectral siren cosmology, reducing biases in Hubble constant measurements from gravitational wave data.
Contribution
It introduces a novel population model using Normalizing Flows trained on synthetic data, improving the accuracy of Hubble constant inference from gravitational wave observations.
Findings
Successfully recovers true distribution of BBH masses
Eliminates systematic biases in Hubble constant estimation
Shows degeneracy between H0 and binary formation channels
Abstract
Binary black hole (BBH) mergers detected through Gravitational Waves (GWs) are a promising probe for the cosmic expansion. These sources are standard sirens for which we can directly measure the luminosity distance, but their redshift is degenerate with the determination of their source masses. In analogy to standard candles, the redshift of standard sirens can be obtained using a calibration based on the source mass spectrum, but without the need for a cosmological ladder. It has been recently shown that a mismodeling of the BBH mass spectrum is very likely to introduce a bias in the determination of the Hubble constant. To tackle this issue, we develop a BBH population model based on Normalizing Flows, trained on synthetic BBH catalogs generated from astrophysical prescriptions, including binaries formed through both isolated stellar evolution and dynamical environments. We validate…
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Taxonomy
TopicsPulsars and Gravitational Waves Research · Gamma-ray bursts and supernovae · Cosmology and Gravitation Theories
