Developing techniques for high redshift quasar selection in HSC Wide Field data

Abstract

Here I present an analysis of a comparison of selection methods for high redshift ($z\gtrsim6$) quasars. In this paper I aim to develop and compare selection methods for high redshift quasars with the aim of preparing for the release of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) data. Through this work I am starting to develop efficient code that will be able to process large data volumes, and produce results with high completeness and purity. The LSST data will be of unprecedented size and depth, providing a greater opportunity for discovering high redshift quasars. However, with the ambitious size of the survey, we expect a very large number of potential contaminants when selecting high redshift quasars. Therefore it is imperative that any selection methods applied to this data have high purity rates. This paper provides the first results of a new tool in the LSST Science Pipelines (available upon the next software release) that can be used for refining high redshift quasar selection criteria, synthetic source injection (SSI). In this paper I perform validation and verification of the SSI pipeline, analyzing fake quasar SED models artificially inserted into existing Hyper Suprime-Cam (HSC) Wide images. The results of validation show the SSI pipeline preserves input coordinates and magnitudes to a high degree of accuracy. With this success, I also give an example of candidate selection, ranked by a reduced $\chi^2$ test on the candidate SEDs versus the quasar models.