The stellar distribution function and local vertical potential from Gaia DR2. Issue 2 (1st March 2021)

• Record Type: Journal Article
• Authors: Li, Haochuan; Widrow, Lawrence M
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 503:Issue 2(2021)
• Page Start: 1586
• Page End: 1598
• Publication Date: 2021-03-01
• Subjects: Galaxy: disc -- Galaxy: evolution -- Galaxy: kinematics and dynamics -- (Galaxy:) solar neighborhood -- Galaxy: structure; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/stab574
• Languages: English
• ISSNs: 0035-8711

Abstract

ABSTRACT: We develop a novel method to simultaneously determine the vertical potential, force, and stellar z − vz phase space distribution function (DF) in our local patch of the Galaxy. We assume that the Solar Neighbourhood can be treated as a one-dimensional (1D) system in dynamical equilibrium and directly fit the number density in the z − vz plane to what we call the rational linear distribution function (RLDF) model. This model can be regarded as a continuous sum of isothermal DFs though it has only one more parameter than the isothermal model. We apply our method to a sample of giant stars from Gaia Data Release 2 and show that the RLDF provides an excellent fit to the data. The well-known phase space spiral emerges in the residual map of the z − vz plane. We use the best-fitting potential to plot the residuals in terms of the frequency and angle of vertical oscillations and show that the spiral maps into a straight line. From its slope, we estimate that the phase spirals were generated by a perturbation ∼540 Myr years ago. We also determine the differential surface density as a function of vertical velocity dispersion, a.k.a. the vertical temperature distribution. The result is qualitatively similar to what was previously found for SDSS/SEGUE G dwarfs. Finally, we address parameter degeneracies and the validity of the 1D approximation. Particularly, the mid-plane density derived from a cold sub-sample, where the 1D approximation is more secure, is closer to literature values than that derived from the sample as a whole.