Distinguishing Orbiting and Infalling Dark Matter Particles with Machine Learning

TL;DR

This paper introduces a machine learning method to classify dark matter particles as orbiting or infalling, improving physical halo definitions and enabling fast, accurate analysis across different cosmologies.

Contribution

The authors develop a decision tree-based classifier using particle radii and velocities, achieving high accuracy and generalizability for dark matter particle classification.

Findings

97% classification accuracy compared to trajectory analysis

Reproduces density profiles out to many virial radii

Model generalizes to different cosmologies

Abstract

Dark matter halos are typically defined as spheres that enclose some overdensity, but these sharp, somewhat arbitrary boundaries introduce non-physical artifacts such as backsplash halos, pseudo-evolution, and an incomplete accounting of halo mass. A more physically motivated alternative is to define halos as the collection of particles that are physically orbiting within their potential well. However, existing methods to classify particles as orbiting or infalling suffer from trade-offs between accuracy, computational cost, and generalizability across cosmologies. We present an efficient, yet accurate, supervised machine learning approach using decision trees. The classification is based on only the particle radii and velocities at two epochs. Compared to detailed analysis of particle trajectories, we find that our model matches the classification of 97\% of particles. Consequently, we are able to quickly and accurately reproduce the density profiles of the orbiting and infalling components out to many virial radii. We demonstrate that our model generalizes to a significantly different cosmology that lies outside the training dataset. We make publicly available both our final model and the code to train similar models.