Mitigating Imaging Systematics for DESI 2024 Emission Line Galaxies and Beyond

TL;DR

This paper develops and tests advanced methods to mitigate imaging systematics in DESI ELG data, ensuring accurate large-scale structure measurements for cosmology, especially at higher redshifts.

Contribution

It introduces a combined forward modeling and neural network approach to derive comprehensive 3D selection functions that reduce imaging systematics effects in DESI ELG analyses.

Findings

Extended treatments are consistent with fiducial results within uncertainties.

Differences due to systematics are smaller than the uncertainties in DESI 2024 measurements.

Full 3D selection functions are essential for higher redshift ELGs ($0.6<z<0.8$).

Abstract

Emission Line Galaxies (ELGs) are one of the main tracers that the Dark Energy Spectroscopic Instrument (DESI) uses to probe the universe. However, they are afflicted by strong spurious correlations between target density and observing conditions known as imaging systematics. We present the imaging systematics mitigation applied to the DESI Data Release 1 (DR1) large-scale structure catalogs used in the DESI 2024 cosmological analyses. We also explore extensions of the fiducial treatment. This includes a combined approach, through forward image simulations in conjunction with neural network-based regression, to obtain an angular selection function that mitigates the imaging systematics observed in the DESI DR1 ELGs target density. We further derive a line-of-sight selection function from the forward model that removes the strong redshift dependence between imaging systematics and low redshift ELGs. Combining both angular and redshift-dependent systematics, we construct a 3D selection function and assess the impact of all selection functions on clustering statistics. We quantify differences between these extended treatments and the fiducial treatment in terms of the measured 2-point statistics. We find that the results are generally consistent with the fiducial treatment and conclude that the differences are far less than the imaging systematics uncertainty included in DESI 2024 full-shape measurements. We extend our investigation to the ELGs at $0.6<z<0.8$, i.e., beyond the redshift range ($0.8<z<1.6$) adopted for the DESI clustering catalog, and demonstrate that determining the full 3D selection function is necessary in this redshift range. Our tests showed that all changes are consistent with statistical noise for BAO analyses indicating they are robust to even severe imaging systematics. Specific tests for the full-shape analysis will be presented in a companion paper.