A Bayesian estimation of the Milky Way's circular velocity curve using Gaia DR3
Sven P\~oder, Mar\'ia Benito, Joosep Pata, Rain Kipper, Heleri Ramler,, Gert H\"utsi, Indrek Kolka, Guillaume F. Thomas

TL;DR
This paper uses Bayesian inference with Gaia DR3 data to accurately determine the Milky Way's circular velocity curve, estimate dark matter distribution, and quantify uncertainties from various sources in a self-consistent manner.
Contribution
It introduces a Bayesian approach to reconstruct the Milky Way's circular velocity curve using Gaia DR3 data, accounting for systematic uncertainties and providing new estimates of dark matter content.
Findings
Circular velocity curve is consistent with being flat within uncertainties.
Estimated circular velocity at the Sun's position is 233 km/s with 7 km/s uncertainty.
Dark matter mass within 14 kpc is approximately 10^11.2 solar masses.
Abstract
Our goal is to calculate the circular velocity curve of the Milky Way, along with corresponding uncertainties that quantify various sources of systematic uncertainty in a self-consistent manner. The observed rotational velocities are described as circular velocities minus the asymmetric drift. The latter is described by the radial axisymmetric Jeans equation. We thus reconstruct the circular velocity curve between Galactocentric distances from 5 kpc to 14 kpc using a Bayesian inference approach. The estimated error bars quantify uncertainties in the Sun's Galactocentric distance and the spatial-kinematic morphology of the tracer stars. As tracers, we used a sample of roughly 0.6 million stars on the red giant branch stars with six-dimensional phase-space coordinates from Gaia data release 3 (DR3). More than 99% of the sample is confined to a quarter of the stellar disc with mean radial,…
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