Author(s)Trick, Wilma Henriette
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AbstractUnderstanding the Milky Way’s present structure and assembly history constitutes a crucial constraint on galaxy formation and evolution theory. Galactic surveys like the Gaia mission provide high-precision measurements of positions, velocities, and chemical abundances of soon millions of stars in the Milky Way disk. Exploiting these high quality data requires sophisticated modeling tools. This PhD thesis is dedicated to the development, characterization, and application of RoadMapping, a dynamical modeling machinery aiming to constrain the Galactic gravitational potential and chemo-orbital distribution function (DF) of the stellar disk. RoadMapping proceeds by modeling the observed discrete 6D phase-space positions of stellar mono-abundance populations (MAPs) by an axisymmetric parameterized potential model and an axisymmetric action-based orbit DF in a full-likelihood Bayesian framework. RoadMapping takes into account the survey’s selection function (SF) and measurement uncertainties. RoadMapping builds on previous work by Bovy & Rix (2013), Binney & McMillan (2011), and Binney (2012a). The first part of this work was published as Trick et al. (2016a) and gives an overview of the RoadMapping machinery. Its characteristics are studied by analyzing a large suite of axisymmetric mock data sets. It is found that RoadMapping constraints on the gravitational potential are robust against minor imperfections in the knowledge of the optimal potential or DF model family, selection effects, or velocity measurement uncertainties, as long as the distance uncertainties of the stars are better than 10%. The second part is based on Trick et al. (2017) and investigates RoadMapping in the presence of spiral arms by modeling data drawn from an N-body simulation snapshot of a disk-dominated galaxy with strong spiral arms by D'Onghia et al. (2013). This provides a realistic test scenario for RoadMapping to model non-axisymmetric data with axisymmetric models. It is found that RoadMapping always recovers a good average model for the gravitational forces at the location of the stars that entered the analysis. The third part applies RoadMapping to real data in the Milky Way. It combines measurements by Gaia-TGAS (Lindegren et al. 2016), RAVE (Kunder et al. 2017), and RAVE-on (Casey et al. 2017). Red clump stars are selected and photometric distances are assigned to them following Bovy et al. (2014). A strategy is devised to setup an SF for this sample that can be used in RoadMapping. The sample consists of 16 MAPs in the low-alpha disk. All MAPs provide independent and consistent constraints on the Milky Way’s gravitational potential, measuring the disk scale length and circular velocity at the Sun to high precision, R_s,disk = 3.01 +/- 0.05 kpc and v_circ(R) = 231.4 +/- 0.7 km/s. The total surface mass density at the Sun that is recovered is, with Sigma_tot,1.1kpc = 98 +/- 3 M_sun/pc^2, larger than previous estimates in the literature, which is attributed, however, to the data. Overall, RoadMapping is a well-tested and robust dynamical modeling machinery, whose preliminary and successful application to Gaia data promises new, precise, and reliable constraints on the Galactic gravitational potential in the near future.
Trick, Wilma Henriette (2017) Action-based Dynamical Modeling for the Milky Way Disk. [Dissertation]