16 research outputs found

    A Dynamical Model of the Inner Galaxy

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    An extension of Schwarzschild's galaxy-building technique is presented that, for the first time, enables one to build Schwarzschild models with known distribution functions (DFs). The new extension makes it possible to combine a DF that depends only on classical integrals with orbits that respect non-classical integrals. With such a combination, Schwarzschild's orbits are used only to represent the difference between the true galaxy DF and an approximating classical DF. The new method is used to construct a dynamical model of the inner Galaxy. The model is based on an orbit library that contains 22168 regular orbits. The model aims to reproduce the three-dimensional mass density of Binney, Gerhard & Spergel (1997), which was obtained through deprojection of the COBE surface photometry, and to reproduce the observed kinematics in three windows - namely Baade's Window and two off-axis fields. The model fits essentially all the available data within the innermost 3 kpc. The axis ratio and the morphology of the projected density contours of the COBE bar are recovered to good accuracy within corotation. The kinematic quantities - the line-of-sight streaming velocity and velocity dispersion, as well as the proper motions when available - are recovered, not merely for the fitted fields, but also for three new fields. The dynamical model deviates most from the input density close to the Galactic plane just outside corotation, where the deprojection of the surface photometry is suspect. The dynamical model does not reproduce the kinematics at the most distant window, where disk contamination may be severe.Comment: 20 pages, 5 gif figures, 11 postscript figures, submitted to MNRAS. Zipped postscript available at http://www-thphys.physics.ox.ac.uk/users/RalfHafner/paper.ps.g

    Learning of Visual Navigation Strategies

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    Hafner VV, Möller R. Learning of Visual Navigation Strategies. In: Quoy M, Gaussier P, Wyatt J, eds. Proc. European Workshop of Learning Robots (EWLR-9), Prague. 2001: 47-56

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