THURSDAY, February 14^th, 2008

Speaker: Xiao Wang, Univ. Maryland, Baltimore

Title: Nonparametric Estimation of Dark Matter Distributions

Abstract: Estimating dark matter distribution is an interesting and challenging problem, which has important consequences for understanding the evolution of the Universe and the structure within it. In this talk, I describe a new, nonparametric analysis of the kinematics of nearby galaxies. The data consist of projected positions and radial velocities of a sample of a few thousand stars. Estimating the mass distribution is converted into a problem of estimating a regression function nonparametrically. We show that the unknown regression function is subject to fundamental shape restrictions which we exploit in our analysis using techniques borrowed from isotonic estimation and spline smoothing.

Simulations indicate the method works well even for small sample size. The technique is applied to new kinematic data for some dwarf galaxies.

Results indicate that dark matter dominates the stellar kinematics of these systems at all radii.