Reliable mass calculation in spherical gravitating systems

TL;DR

This paper introduces a new dynamical modelling approach that reduces the mass-anisotropy degeneracy in spherical systems, utilizing AI techniques to improve dark matter mass reconstruction from limited observational data.

Contribution

It presents a novel method combining statistical inference and AI to accurately determine dark matter profiles without assuming density and anisotropy functions.

Findings

Successfully distinguishes dark matter distributions in simulations

Recovers anisotropy and mass profiles from small datasets

Reduces degeneracy in mass modeling of spherical systems

Abstract

We present an innovative approach to the methodology of dynamical modelling, allowing practical reconstruction of the underlying dark matter mass without assuming both the density and anisotropy functions. With this, the mass-anisotropy degeneracy is reduced to simple model inference, incorporating the uncertainties inherent with observational data, statistically circumventing the mass-anisotropy degeneracy in spherical collisionless systems. We also tackle the inadequacy that the Jeans method of moments has on small datasets, with the aid of Generative Adversarial Networks: we leverage the power of artificial intelligence to reconstruct non-parametrically the projected line-of-sight velocity distribution. We show with realistic numerical simulations of dwarf spheroidal galaxies that we can distinguish between competing dark matter distributions and recover the anisotropy and mass profile of the system.