Probing the peak of star formation with the stochastic background of binary black hole mergers
Nico Bers, Sylvia Biscoveanu

TL;DR
This study uses a Bayesian method to analyze simulated LIGO data, revealing that weak, undetectable black hole mergers significantly inform the stochastic gravitational-wave background and high-redshift star formation history.
Contribution
It introduces a Bayesian background search technique that models an unknown number of mergers, improving inference of high-redshift binary black hole populations from weak signals.
Findings
Weak mergers contribute to background detection.
Weak mergers help constrain high-redshift star formation.
Method enhances understanding of early universe black hole mergers.
Abstract
Although the LIGO-Virgo-KAGRA collaboration detects many individually resolvable gravitational-wave events from binary black hole mergers, those that are too weak to be identified individually contribute to a stochastic gravitational-wave background. Unlike the standard cross-correlation search for excess correlated power, a Bayesian search method that models the background as a superposition of an unknown number of mergers enables simultaneous inference of the properties of high-redshift binary black hole populations and accelerated detection of the background. In this work, we apply this templated background search method to one day of simulated data at current LIGO Hanford-Livingston detector network sensitivity to determine whether the weakest mergers contribute information to the detection of the background and to the constraint on the merger redshift distribution at high…
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Taxonomy
TopicsPulsars and Gravitational Waves Research · Gamma-ray bursts and supernovae · Statistical Mechanics and Entropy
