Campus, Varian 355
Lin: Dark Matter Substructures are interesting since they can reveal the properties of dark matter, especially the cold dark matter small-scale problems such as missing satellites problem. In recent years, it has become possible to detect individual dark matter subhalos near images of strongly lensed extended background galaxies. In this talk, I would discuss the possibility of using deep neural networks to detect dark matter subhalos, and showing some preliminary result with simulated data.
Zhao: In this talk, I will introduce the produce of large sets of mock catalogues for the targets of eBOSS, for estimating the covariance matrix of large-scale clustering measurements, as well as investigation of systematical effects. To this end, we use the methodology, EZmock, developed by Chuang et al. 2015 (https://arxiv.org/abs/1409.1124). By extending the Zel’dovich approximation density field with a proper bias model (the relation between tracers and the density field), we can accurately reproduce the clustering of the observed data including 2- and 3- point clustering statistics in redshift space with very low computational costs. In addition, by constructing the mocks with the same initial condition, but at different redshift snapshots, we include also the light-cone effect (e.g. evolution of bias) in these mock catalogues. Furthermore, the different tracers (LRG/ELG/QSO) populated from the same initial density fields show a consistent cross correlation with that of data.