# University of Hertfordshire

## The Milky Way bar/bulge in proper motions: a 3D view from VIRAC & Gaia

Research output: Contribution to journalArticle

### Documents

• stz2382

Final published version, 6.26 MB, PDF document

• Jonathan P. Clarke
• Christopher Wegg
• Ortwin Gerhard
• Leigh C. Smith
• Philip Lucas
• Shola M. Wylie
Original language English 3519-3538 20 Monthly Notices of the Royal Astronomical Society 489 3 10 Sep 2019 https://doi.org/10.1093/mnras/stz2382 Published - Nov 2019

### Abstract

We have derived absolute proper motions of the entire Galactic bulge region from VIRAC and Gaia. We present these as both integrated on-sky maps and, after isolating standard candle red clump (RC) stars, as a function of distance using RC magnitude as a proxy. These data provide a new global, 3-dimensional view of the Milky Way barred bulge kinematics. We find a gradient in the mean longitudinal proper motion, $\mu_l$, between the different sides of the bar, which is sensitive to the bar pattern speed. The split RC has distinct proper motions and is colder than other stars at similar distance. The proper motion correlation map has a quadrupole pattern in all magnitude slices showing no evidence for a separate, more axisymmetric inner bulge component. The line-of-sight integrated kinematic maps show a high central velocity dispersion surrounded by a more asymmetric dispersion profile. $\sigma_{\mu_l} / \sigma_{\mu_b}$ is smallest, $\sim1.1$, near the minor axis and reaches $\sim1.4$ near the disc plane. The integrated  pattern signals a superposition of bar rotation and internal streaming motion, with the near part shrinking in latitude and the far part expanding. To understand and interpret these remarkable data, we compare to a made-to-measure barred dynamical model, folding in the VIRAC selection function to construct mock maps. We find that our model of the barred bulge, with a pattern speed of 37.5 $\mathrm{km \, s^{-1} \, kpc^{-1}}$, is able to reproduce all observed features impressively well. Dynamical models like this will be key to unlocking the full potential of these data.