PixelPop: High Resolution Nonparameteric Inference of Gravitational-Wave Populations in Multiple Dimensions

TL;DR

PixelPop is a high-resolution Bayesian nonparametric method that infers complex, multidimensional gravitational-wave source populations with minimal assumptions, improving understanding of their formation channels.

Contribution

It introduces PixelPop, a novel dense binning approach that accurately recovers joint distributions and correlations in gravitational-wave populations without relying on strong parametric models.

Findings

PixelPop accurately recovers true populations within uncertainties.

It effectively detects and quantifies parameter correlations.

The method provides conservative estimates of population features.

Abstract

The origins of merging compact binaries observed by gravitational-wave detectors remains highly uncertain. Several astrophysical channels may contribute to the overall merger rate, with distinct formation processes imprinted on the structure and correlations in the underlying distributions of binary source parameters. In the absence of confident theoretical models, the current understanding of this population mostly relies on simple parametric models that make strong assumptions and are prone to misspecification. Recent work has made progress using more flexible nonparametric models, but detailed measurement of the multidimensional population remains challenging. In pursuit of this, we present PixelPop-a high resolution Bayesian nonparametric model to infer joint distributions and parameter correlations with minimal assumptions. PixelPop densely bins the joint parameter space and directly infers the merger rate in each bin, assuming only that bins are coupled to their nearest neighbors. We demonstrate this method on mock populations with and without bivariate source correlations, employing several statistical metrics for information gain and correlation significance to quantify our nonparametric results. We show that PixelPop correctly recovers the true populations within posterior uncertainties and offers a conservative assessment of population-level features and parameter correlations. Its flexibility and tractability make it a useful data-driven tool to probe gravitational-wave populations in multiple dimensions.