  The quasar target selection for the upcoming survey of the Dark Energy
Spectroscopic Instrument (DESI) will be fixed for the next five years. The aim
of this work is to validate the quasar selection by studying the impact of
imaging systematics as well as stellar and galactic contaminants, and to
develop a procedure to mitigate them. Density fluctuations of quasar targets
are found to be related to photometric properties such as seeing and depth of
the Data Release 9 of the DESI Legacy Imaging Surveys. To model this complex
relation, we explore machine learning algorithms (Random Forest and Multi-Layer
Perceptron) as an alternative to the standard linear regression. Splitting the
footprint of the Legacy Imaging Surveys into three regions according to
photometric properties, we perform an independent analysis in each region,
validating our method using eBOSS EZ-mocks. The mitigation procedure is tested
by comparing the angular correlation of the corrected target selection on each
photometric region to the angular correlation function obtained using quasars
from the Sloan Digital Sky Survey (SDSS)Data Release 16. With our procedure, we
recover a similar level of correlation between DESI quasar targets and SDSS
quasars in two thirds of the total footprint and we show that the excess of
correlation in the remaining area is due to a stellar contamination which
should be removed with DESI spectroscopic data. We derive the Limber parameters
in our three imaging regions and compare them to previous measurements from
SDSS and the 2dF QSO Redshift Survey.
