[Paper Review] Angular clustering properties of the DESI QSO target selection using DR9 Legacy Imaging Surveys

E. Chaussidon, Christophe Yèche+13 more|arXiv (Cornell University)|Aug 8, 2021

Galaxies: Formation, Evolution, PhenomenaPhysics and Astronomy54 references38 citations

TL;DR

This paper develops and validates a machine learning-based mitigation procedure to correct angular clustering systematics in the DESI QSO target selection using DR9 Legacy Imaging Surveys. By applying Random Forest and Multi-Layer Perceptron models to correct for photometric systematics and stellar contamination—particularly from the Sagittarius Stream—it achieves angular correlation functions consistent with SDSS DR16 quasars in two-thirds of the footprint, validating the robustness of the target selection for cosmological clustering analyses.

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.

Motivation & Objective

• . The primary objective is to validate the DESI QSO target selection method by assessing and correcting for imaging systematics and stellar contamination.
• The study aims to ensure that large-scale clustering measurements from the DESI survey are not biased by photometric systematics or target selection effects.
• It seeks to develop a robust, transferable method for correcting angular clustering systematics in spectroscopic target samples using imaging systematics and machine learning.
• The work provides a proof of concept for correcting systematics in the DESI QSO sample prior to spectroscopic follow-up, ensuring cosmologically unbiased results.

Proposed method

• . The authors split the DESI Legacy Imaging Survey footprint into three regions based on photometric properties: North, South, and DES (Dec. > −30).
• They apply machine learning models—Random Forest and Multi-Layer Perceptron—to model the non-linear relationship between target density fluctuations and imaging systematics such as seeing, depth, and PSF.
• Correction weights are derived from the trained models and applied to the target sample to mitigate systematics in each region independently.
• The method is validated using eBOSS EZ-mocks and compared against the angular correlation function of SDSS DR16 quasars to assess cosmological consistency.
• Limber parameters (r₀, γ) are derived for each region and compared to prior measurements from SDSS and 2dF QSO surveys.
• A stellar contamination map based on the Sagittarius Stream is used to identify and assess the impact of unresolved stellar contamination on clustering.

Experimental results

Research questions

• RQ1 . To what extent do photometric systematics such as seeing and depth in the z and W2 bands affect the angular clustering of DESI QSO targets?
• RQ2 . How effective is a machine learning-based correction (Random Forest and Multi-Layer Perceptron) in mitigating non-linear systematics compared to linear regression?
• RQ3 . Can the corrected DESI QSO target selection achieve angular clustering properties consistent with those of spectroscopically confirmed quasars from SDSS DR16?
• RQ4 . What is the contribution of stellar contamination—particularly from the Sagittarius Stream—to excess clustering in the angular correlation function?
• RQ5 . To what extent can imaging-based corrections alone resolve systematics, and when is spectroscopic follow-up essential?

Key findings

• . After applying the mitigation procedure, the angular correlation function of DESI QSO targets in the North and South regions matches that of SDSS DR16 quasars within 1σ uncertainty.
• . The corrected DESI QSO target selection achieves a Limber amplitude r₀ = 7.49 ± 0.57 h⁻¹Mpc in the North and r₀ = 10.33 ± 0.84 h⁻¹Mpc in the South, consistent with SDSS measurements.
• . In the DES region (Dec. > −30), the corrected r₀ = 6.76 ± 0.58 h⁻¹Mpc, also consistent with SDSS and 2dF QSO survey results.
• . The excess clustering in the Sagittarius Stream region persists after correction and is attributed to unresolved stellar contamination from faint, previously undetected stars.
• . The method successfully reduces systematics in two-thirds of the footprint, demonstrating that imaging-based corrections can achieve cosmologically relevant accuracy.
• . The study confirms that spectroscopic follow-up is essential to fully remove stellar contamination and achieve optimal clustering measurements.