On Microlensing Event Rates and Optical Depth toward the Galactic Center

Details On Microlensing Event Rates and Optical Depth toward the Galactic Center On Microlensing Event Rates and Optical Depth toward the Galactic Center

1998-06-28

arxiv.org/abs/astro-ph/9806368v1

Astronomy and Astrophysics

Astrophysics

21 pages, 15 figures, Uses aas2pp4.sty

Scientific paper

10.1086/306490

The dependence of microlensing time scale frequency distributions and optical depth toward the galactic center on galactic model parameters is explored in detail for a distribution of stars consisting of the Zhao (1996) bar and nucleus and the Bahcall and Soneira (1980) double exponential disk. The high sensitivity of these two microlensing measures to the circular velocity model, velocity dispersions, bulge mass, direction of the line of sight, bar axis orientation, star spatial distribution and the stellar mass function means no single galaxy property can be constrained very well without constraining most of the others. The model time scale frequency distributions are compared with that determined empirically by the MACHO group throughout. Although the MACHO empirical data are matched quite well with a nominal velocity model and with a mass function only of hydrogen burning stars that varies as m^{-2.2} to m^{-2.5} in the M star region, uncertainties in galactic structure, kinematics and content together with the paucity of published microlensing data preclude any claim of the model representing the real world. A variation of the mass function \sim m^{-1} in the M star region obtained from recent star counts, both local and in the galactic bulge, fails to yield a sufficient number of short time scale events compared to the MACHO data. The high sensitivity of the microlensing measures to the direction of the line of sight may mean that sufficient microlensing data is already in hand to constrain the bar distribution of stars. The procedure developed here for determining the time scale frequency distribution is particularly convenient for rapidly incorporating model changes as data from all sources continues to accumulate.

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