{"paper":{"title":"DESI Data Release 2 ELGs: Property-dependent subsamples, imaging systematics, and clustering","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.GA"],"primary_cat":"astro-ph.CO","authors_text":"A. de la Macorra, A. D. Myers, A. J. Ross, A. Kremin, A. Meisner, B. A. Weaver, B. Dey, C. Hahn, C. Lamman, C. Poppett, D. Bianchi, D. Brooks, D. Schlegel, E. Sanchez, F. Prada, G. Gutierrez, G. Niz, G. Rossi, G. Tarle, H. Zou, I. Perez-Rafols, J. Aguilar, J. A. Newman, J. E. Forero-Romero, J. Guy, J. Moustakas, J. Silber, K. S. Dawson, L. Le Guillou, M. Ishak, M. Landriau, M. Manera, O. Lahav, R. Joyce, R. Miquel, S. Ahlen, S. Ferraro, S. Gontcho A Gontcho, S. Juneau, S. Nadathur, S. Saito, T. Claybaugh, T. Hagen, W. J. Percival, Z. Zheng","submitted_at":"2026-06-17T01:09:08Z","abstract_excerpt":"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 i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.18581","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.18581/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}