# Unmodelled Clustering Methods for Gravitational Wave Populations of Compact Binary Mergers

## TL;DR

This study demonstrates that Gaussian mixture model clustering can identify the number and properties of sub-populations in gravitational-wave sources, requiring around 400 detections in challenging cases with similar mass but different spins.

## Contribution

The paper introduces an unmodelled clustering approach using Gaussian mixture models to analyze gravitational-wave data, aiding in identifying sub-populations without prior assumptions.

## Key findings

- Approximately 400 detections needed to distinguish sub-populations with similar masses
- Clustering effectively recovers sub-population properties from simulated data
- Method works with mass and spin parameters, even with poorly constrained spins

## Abstract

The mass and spin distributions of compact binary gravitational-wave sources are currently uncertain due to complicated astrophysics involved in their formation. Multiple sub-populations of compact binaries representing different evolutionary scenarios may be present among sources detected by Advanced LIGO and Advanced Virgo. In addition to hierarchical modelling, unmodelled methods can aid in determining the number of sub-populations and their properties. In this paper, we apply Gaussian mixture model clustering to 1000 simulated gravitational-wave compact binary sources from a mixture of five sub-populations. Using both mass and spin as input parameters, we determine how many binary detections are needed to accurately determine the number of sub-populations and their mass and spin distributions. In the most difficult case that we consider, where two sub-populations have identical mass distributions but differ in their spin, which is poorly constrained by gravitational-wave detections, we find that ~ 400 detections are needed before we can identify the correct number of sub-populations.