Mitigating imaging systematics with image simulation

Hui Kong, Ashley Ross, Klaus Honscheid, Anna Porredon, and DESI Collaborations


This work presents the development and applications of a new image simulation tool, Obiwan, which which can be used to determine the selection function of a galaxy redshift survey and calculate 3-dimensional (3D) clustering statistics. This is a forward model of the process by which images of the night sky are transformed into a 3D large–scale structure catalog. The photometric pipeline automatically detects and models galaxies and then generates a catalog of such galaxies with detailed information for each one of them, including their location, redshift and so on. Systematic biases in the imaging data are therefore imparted into the catalogs and must be accounted for in any scientific analysis of their information content. Obiwan simulates this process for samples selected from the DESI Legacy Surveys imaging data. This imaging data is used to select target samples for the next-generation Dark Energy Spectroscopic Instrument (DESI) experiment. We discuss the design of the Obiwan software and show an application on a portion of the SDSS-IV extend Baryon Oscillation Spectroscopic Survey Emission Line Galaxies (ELG) sample. Systematic biases in the data are clearly identified and removed. We compare the 3D clustering results to those obtained by the map–based approach applied to the full eBOSS sample. We find the results are consistent, thereby validating the eBOSS ELG catalogs. We also show how Obiwan can be used with the DESI sample of large red galaxies (LRG).