DESI Data Release 2 ELGs: Property-dependent subsamples, imaging systematics, and clustering

arXiv:2606.18581 · astro-ph.CO, astro-ph.GA · Submitted 2026-06-17 · Read on arXiv

T. Hagen, K. S. Dawson, Z. Zheng, J. Aguilar, S. Ahlen, D. Bianchi, D. Brooks, T. Claybaugh, A. de la Macorra, B. Dey, S. Ferraro, J. E. Forero-Romero, S. Gontcho A Gontcho, G. Gutierrez, J. Guy, C. Hahn, M. Ishak, R. Joyce, S. Juneau, A. Kremin, O. Lahav, C. Lamman, M. Landriau, L. Le Guillou, M. Manera, A. Meisner, R. Miquel, J. Moustakas, A. D. Myers, S. Nadathur, J. A. Newman, G. Niz, W. J. Percival, C. Poppett, F. Prada, I. Perez-Rafols, A. J. Ross, G. Rossi, S. Saito, E. Sanchez, D. Schlegel, J. Silber, G. Tarle, B. A. Weaver, H. Zou

astro-ph.CO, astro-ph.GA

Submitted: 2026-06-17

Comments: 35 pages, 13 figures

Code: https://github.com/desihub/redrockhttps:

License: http://creativecommons.org/licenses/by/4.0/

The gist: Using emission-line galaxies (ELGs) from the Dark Energy Spectroscopic Instrument (DESI) Data Release 2, we evaluate a property-dependent correction to imaging systematics.

Terminology

Abstract

Using emission-line galaxies (ELGs) from the Dark Energy Spectroscopic Instrument (DESI) Data Release 2, we evaluate a property-dependent correction to imaging systematics. We derive systematic weights following the same linear regression method used for other DESI tracers, but do so separately on ELG subsamples to provide a physically-informed alternative to the fiducial, neural-network-based approach. In doing so, we show that the deeper imaging in the Dark Energy Survey (DES) footprint leads to a higher overall number density but a lack of targets with extreme g-r and r-z colors. ELGs in the DES region also show a distinct redshift distribution when subsampled by position in the g-r vs. r-z plane. To address these effects, we implement a separate treatment of the DES footprint within the DESI catalog production pipeline, which is generally well-motivated and, in some cases, imperative for accurate clustering measurements. With DES treated separately, we find that property-dependent systematic weights further mitigate spurious clustering signal in about 10% of subsamples, while the fiducial scheme remains optimal for the full sample.

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