Title
Estimating Dark Matter Distributions
Speaker
Xiao Wang, University of Michigan
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 will 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 a sample of 181 stars in Fornax galaxy. We show that the galaxy is dominated by dark matter.
Meet the speaker in Room 212 Cockins Hall at 4:30 p.m. Refreshments will be served.
