📝 SummarXiv

Abstract

Mass-modeling methods are used to infer the gravitational field of stellar systems, from globular clusters to giant elliptical galaxies. While many methods exist, most require assumptions about the form of the underlying distribution function or data binning that leads to loss of information. With only line-of-sight (LOS) data, many methods suffer from the well-known mass-anisotropy degeneracy. To overcome these limitations, we develop a new, publicly available mass-modeling method, GravSphere2. This combines individual stellar velocities from LOS and proper motion (PM) measurements to solve the Jeans equations up to fourth order, without any data binning. Using flexible functional forms for the anisotropy profiles at second and fourth order, we show how including additional constraints from a new observable - fourth-order PMs - fully closes the system of equations, breaking the mass-anisotropy degeneracy at all orders. We test our method on mock data for dwarf galaxies, showing how GravSphere2 improves on previous methods. GravSphere2 recovers the mass density, stellar velocity anisotropy, and logarithmic slope of the mass density profile within its quoted 95% confidence intervals across almost all mocks over a wide radial range (0.1 < R/Rhalf < 10). We find GravSphere2 outperforms simple mass estimators, suggesting that it is worth using even when only a few LOS velocities are available. With 1,000 tracers without PMs, GravSphere2 recovers the logarithmic density slope at Rhalf with 12% (25%) statistical errors for cuspy (cored) mock data, enabling a distinction between the two. Including PMs, this improves to 8% (12%). With just 100 tracers and no PMs, we recover slopes with ~ 30% (20%) errors. GravSphere2 will be a valuable new tool to hunt for black holes and dark matter in spherical stellar systems, from globular clusters and dwarf galaxies to giant ellipticals and galaxy clusters.