Mean Mass Density near the Sun from the Divergence Theorem and Pulsar Accelerations

TL;DR

This paper presents a novel non-parametric method using pulsar accelerations and the divergence theorem to estimate the mean mass density around the Sun, aiming to better understand local dark matter distribution.

Contribution

It introduces a new approach based on pulsar acceleration measurements and extends the shell theorem to spherical harmonics for density asymmetry analysis.

Findings

Results align with previous mass density estimates.

Method currently limited by data precision and quantity.

Potential to detect dark matter distribution asymmetries.

Abstract

We introduce a new, non-parametric method for estimating the mass enclosed within a sphere of arbitrary radius centered on the Sun. The method is based on the divergence theorem as applied to measurements of the line-of-sight accelerations of millisecond pulsars. We describe a procedure for inferring the mean mass density within a sphere of a given radius centered on the Sun and find results that are consistent with previous analyses. When combined with a model for the distribution of baryons, this provides the mean mass density of dark matter as a function of distance from the Sun, rather than a single value as is typically reported by kinematic studies. However, with the present pulsar data, the method cannot unambiguously measure a signal from the local distribution of dark matter at this time; such a measurement is expected to soon become possible as the amount of pulsar acceleration data grows and its precision improves. We derive an extension of the well-known shell theorem to a spherical-harmonics expansion of the density and potential, and use the result to obtain estimates for density asymmetries with respect to the Galactic midplane from the observed acceleration data. The predicted asymmetries do not follow the observed distribution of MW disk stars or gas; this can potentially be explained by a non-uniform distribution of dark matter in the Solar neighborhood.