Recovery of DESI BGS redshift measurements using machine learning (2019)
The DESI (Dark Energy Spectroscopic Instrument) project will conduct a five-year survey designed to cover 14.000 deg² by studying baryon acoustic oscillations (BAO) and redshift-space distortions (RSD). DESI needs simulations for its design, development, and operation, it uses simulations to evaluate the data pipelines and measurements of redshifts from the spectrometers. In particular, DESI uses a simulated survey of mock galaxies to compare their redshifts with the -also simulated- redshift measurements of DESI. These redshift measurements, however, present differences with respect to the true redshifts values of the mock galaxies. It is necessary to correct these measurements so that the instrument can work properly when tested in the real world. The objective of this monograph is, therefore, to apply machine learning (ML) methods to the simulation data to recover the true redshift measurements of the Bright Galaxy Sample using observational variables as input. First, we pre-process the data and select