Impact of Spin Priors on the Population Inference of Merging Binary Black Holes

TL;DR

This paper investigates how different prior assumptions on black hole spins affect the inference of their population properties from gravitational-wave data, proposing a new prior that improves accuracy especially for aligned spins.

Contribution

The authors introduce a novel spin prior uniform in effective spin parameters, enhancing the accuracy of population inference from GW data compared to traditional priors.

Findings

The proposed prior better recovers the true spin distribution in simulations.

Prior choice influences the inferred population, especially for aligned spins.

The new prior also improves mass distribution recovery due to spin-mass correlations.

Abstract

The spins of merging binary black holes (BBHs) inferred from gravitational-wave (GW) observations provide key insights into their formation channels. However, spin parameters are typically weakly constrained from data, and their inferred values are often strongly influenced by the assumed prior in Bayesian analyses. A commonly used prior, uniform in spin magnitudes and isotropic in spin directions, assigns vanishing probability density to spin-orbit-aligned configurations, potentially biasing inferences for BBH parameters. The prior choice can also affect population-level analyses by degrading the convergence of Monte Carlo integrations used to evaluate the likelihood in hierarchical Bayesian inference. In this work, we propose a novel spin prior that is uniform in the effective spin parameters Xeff and Xp, two spin combinations that can be relatively well measured from GW data, conditioned on the mass ratio. Using simulated BBH populations, we show that the inferred spin population can depend on the choice of prior, and that the proposed prior more accurately recovers the underlying spin population, particularly when the true distribution favors aligned-spin configurations. Because mass and spin measurements are correlated, our prior also enables a more accurate recovery of the underlying mass distribution.