To beta or not to beta: can higher-order Jeans analysis break the mass-anisotropy degeneracy in simulated dwarfs?

TL;DR

This study evaluates GravSphere, a non-parametric higher-order Jeans analysis method, on simulated dwarf galaxies to assess its accuracy in recovering dark matter profiles and breaking the mass-anisotropy degeneracy.

Contribution

The paper demonstrates that GravSphere provides accurate mass and density profiles for simulated dwarfs, with improved uncertainties over traditional Jeans methods, and explores the impact of priors and data constraints.

Findings

Mass profiles are recovered within 10% bias for CDM dwarfs.

Uncertainties are smaller than traditional Jeans methods.

Bias towards cuspy profiles in SIDM dwarfs due to data constraints.

Abstract

We test a non-parametric higher-order Jeans analysis method, GravSphere, on 32 simulated dwarf galaxies comparable to classical Local Group dwarfs like Fornax. The galaxies are selected from the APOSTLE suite of cosmological hydrodynamics simulations with Cold Dark Matter (CDM) and Self-Interacting Dark Matter (SIDM) models, allowing us to investigate cusps and cores in density distributions. We find that, for CDM dwarfs, the recovered enclosed mass profiles have a bias of no more than 10 per cent, with a 50 per cent scatter in the inner regions and a 20 per cent scatter near the half-light radius, consistent with standard mass estimators. The density profiles are also recovered with a bias of no more than 10 per cent and a scatter of 30 per cent in the inner regions. For SIDM dwarfs, the mass and density profiles are recovered within our 95 per cent confidence intervals, but are biased towards cuspy dark matter distributions. This is mainly due to a lack of sufficient constraints from the data. We explore the sources of scatter in the accuracy of the recovered profiles and suggest a $\chi^2$ statistic to separate successful models from biased ones. Finally, we show that the uncertainties on the mass profiles obtained with GravSphere are smaller than those for comparable Jeans methods, and that they can be further improved if stronger priors, motivated by cosmological simulations, are placed on the velocity anisotropy. We conclude that GravSphere is a promising Jeans-based approach for modelling dark matter distributions in dwarf galaxies.