Speaker
Sung Hak Lim
(Rutgers University)
Description
We introduce a generative adversarial network for analyzing the dark matter distribution of a dwarf spheroidal galaxy.
The mock data generator for dwarf spheroidal galaxies in the spherically symmetric case has three functional parameters: the number density of stars, the density of dark matter, and velocity anisotropy.
The generator will be adversarially trained on a mock dataset, which contains only the line-of-sight information, to identify the dataset's unknown dark matter distribution under given velocity anisotropy.
We will explain how we implement specialized classifiers, generators cooperating with the spherical Jeans equation, and regularizers to avoid less physical solutions.
Primary authors
Sung Hak Lim
(Rutgers University)
Prof.
Mihoko Nojiri
(Theory Center, IPNS, KEK)
Dr
Shigeki Matsumoto
(Kavli IPMU)
Kohei Hayashi
(Kavli Institute for the Physics and Mathematics of the Universe, The University of Tokyo)
Shunichi Horigome
(Kavli IPMU)
