First test of a method to detect halo objects in the Milky Way using real data

Abstract

UFDGs are an interesting object of study, as they are systems dominated by dark matter and may offer insight into current problems of the [lambda]CDM paradigm. In this work, we have focused on preparing some Python routines for the treatment of the next Gaia data release, DR2, coming up in the spring of 2018. The work consisted mostly in the implementation of a program that divides the sky in cells, and in designing and applying color cuts to the data. These codes were used to select data of several objects in the Galactic halo from the catalogs HSOY, SDSS and GPS1. The data was then used to test, for the first time with real data, a method to detect halo objects. The method is based on the Wavelet Transform (WT) and uses positions and proper motions to detect overdensities. We do a first assessment of the performance of the method on the selected halo objects. Our results show that the method works with the current available data identifying objects such as a globular cluster (Palomar 5 ) and a classical dwarf galaxy (Draco), although it does not succeed when trying to identify UFDGs. It is expected that with an improvement on the data precision, the method will work with DR2.