{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/elg-spectroscopic-systematics-analysis-of-the","title":"ELG Spectroscopic Systematics Analysis of the DESI Data Release 1","arxiv_id":"2405.16657","date":"2024-05-26","proceeding":null,"authors":["Jiaxi Yu","Ashley J. Ross","Antoine Rocher","Otávio Alves","Arnaud de Mattia","Daniel Forero-Sánchez","Jean-Paul Kneib","Alex Krolewski","TingWen Lan","Michael Rashkovetskyi","Jessica Nicole Aguilar","Steven Ahlen","Stephen Bailey","David Brooks","Edmond Chaussidon","Todd Claybaugh","Axel de la Macorra","Arjun Dey","Biprateep Dey","Peter Doel","Kevin Fanning","Jaime E. Forero-Romero","Enrique Gaztañaga","Satya Gontcho A Gontcho","Klaus Honscheid","Cullan Howlett","Stephanie Juneau","Theodore Kisner","Anthony Kremin","Andrew Lambert","Martin Landriau","Laurent Le Guillou","Michael E. Levi","Marc Manera","Paul Martini","Aaron Meisner","Ramon Miquel","John Moustakas","Eva-Maria Mueller","Andrea Muñoz-Gutiérrez","Adam D. Myers","Jundan Nie","Gustavo Niz","Nathalie Palanque-Delabrouille","Will J. Percival","Claire Poppett","Francisco Prada","Mehdi Rezaie","Graziano Rossi","Eusebio Sanchez","Edward F. Schlafly","David Schlegel","Michael Schubnell","Hee-Jong Seo","David Sprayberry","Gregory Tarlé","Benjamin A. Weaver","Pauline Zarrouk","Cheng Zhao","Rongpu Zhou","Hu Zou"],"abstract":"Dark Energy Spectroscopic Instrument (DESI) uses more than 2.4 million Emission Line Galaxies (ELGs) for 3D large-scale structure (LSS) analyses in its Data Release 1 (DR1). Such large statistics enable thorough research on systematic uncertainties. In this study, we focus on spectroscopic systematics of ELGs. The redshift success rate ($f_{\\rm goodz}$) is the relative fraction of secure redshifts among all measurements. It depends on observing conditions, thus introduces non-cosmological variations to the LSS. We, therefore, develop the redshift failure weight ($w_{\\rm zfail}$) and a per-fibre correction ($\\eta_{\\rm zfail}$) to mitigate these dependences. They have minor influences on the galaxy clustering. For ELGs with a secure redshift, there are two subtypes of systematics: 1) catastrophics (large) that only occur in a few samples; 2) redshift uncertainty (small) that exists for all samples. The catastrophics represent 0.26\\% of the total DR1 ELGs, composed of the confusion between O\\,\\textsc{ii} and sky residuals, double objects, total catastrophics and others. We simulate the realistic 0.26\\% catastrophics of DR1 ELGs, the hypothetical 1\\% catastrophics, and the truncation of the contaminated $1.31<z<1.33$ in the \\textsc{AbacusSummit} ELG mocks. Their $P_\\ell$ show non-negligible bias from the uncontaminated mocks. But their influences on the redshift space distortions (RSD) parameters are smaller than $0.2\\sigma$. The redshift uncertainty of \\Yone ELGs is 8.5 km/s with a Lorentzian profile. The code for implementing the catastrophics and redshift uncertainty on mocks can be found in https://github.com/Jiaxi-Yu/modelling_spectro_sys.","url_abs":"https://arxiv.org/abs/2405.16657v6","url_pdf":"https://arxiv.org/pdf/2405.16657v6.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"elg-spectroscopic-systematics-analysis-of-the","repo_url":"https://github.com/jiaxi-yu/modelling_spectro_sys","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}