Inference of Dark Matter Density Profiles of Dwarf Spheroidal Galaxies via Distribution Functions

2019-11-22T20:57:59Z (GMT) by Mao Sheng Liu
Dark matter consist of about 25% of our universe, yet the nature of the dark matter is still unknown. Our current understanding of the cosmology is presented in the theory of
CDM. The theory predicts a cold dark matter (CDM). It is very successful at explaining large-scale structures in the distribution of galaxies and of the cosmic microwave background. However, at small-scale there are controversies. One persistent controversy regards the dark
matter density profi le at the center of the dwarf galaxy. The theory of CDM predicts a steep profi le that diverges as r-1, while the observations tend to suggest a flatter pro file.
The resolution of this controversy could provide a milestone in our understanding of the nature of dark matter. This work is intended to contribute to that eff ort by studying
the dark matter density profi les of the dwarf spheroidal galaxies (dSph). Dwarf satellite galaxies around Milky Way are some of the most dark matter dominated system we know, and without active star formation they are excellent laboratories to resolve this dichotomy. This thesis presents new methodology that analyzes data with foreground contamination to create the first fully consistent inferences of dSph mass density pro file. We analyze Fornax
and Sculptor dSph as the fi rst applications. Direct Bayesian inference is made with the kinematic data of each galaxy, using both Osipkov-Merritt and Strigari Frenk and White
(SFW) stellar distribution functions. The result shows that Fornax has enough data to constrain the dark matter central density profi le. It also shows that depending on the
consistency of modeling foreground contaminants, totally opposite results in central density profi le can be obtained. That shows the importance and the need for consistent treatment of foreground contaminants in future analysis. For the cases where likelihood calculation becomes intractable, kernel density estimation is applied to sample points to approximate the underlying density. Test inferences show that the approximation can reliably recover dark matter density profi les. Machine learning methods are also applied to address this challenge. Mixture Density Network (MDN) shows great promise, although improvement in training is needed to narrow down the prediction uncertainty.