Angular clustering properties of the DESI QSO target selection using DR9 Legacy Imaging Surveys

TL;DR

This study validates DESI quasar target selection by analyzing imaging systematics and contaminants, employing machine learning for mitigation, and comparing angular correlations to SDSS data to ensure reliable quasar clustering measurements.

Contribution

It introduces a machine learning-based mitigation procedure for imaging systematics in DESI quasar target selection, validated across different survey regions and compared with SDSS data.

Findings

Successful mitigation of imaging systematics using machine learning methods.

Recovered quasar clustering signals consistent with SDSS in most survey regions.

Identified stellar contamination as a key factor in excess correlations in some areas.

Abstract

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.