{"paper":{"title":"Local primordial non-Gaussianity from the large-scale clustering of photometric DESI luminous red galaxies","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG","physics.comp-ph","physics.data-an"],"primary_cat":"astro-ph.CO","authors_text":"Aaron Meisner, Adam Myers, Alex Krolewski, Andreu Font-Ribera, Anna Porredon, Arnaud de Mattia, Ashley J. Ross, Axel de la Macorra, Benedict Bahr-Kalus, Benjamin Alan Weaver, Christophe Y\\`eche, Claire Poppett, David Brooks, Dragan Huterer, Edmond Chaussidon, Eusebio Sanchez, Eva-Maria Mueller, Florian Beutler, Graziano Rossi, Gregory Tarl\\'e, Hee-Jong Seo, Hui Kong, Hu Zou, Jaime E. Forero-Romero, Jeffrey A. Newman, Jessica Nicole Aguilar, Jose Bermejo-Climent, Julien Guy, Jundan Nie, Klaus Honscheid, Kyle Dawson, Lado Samushia, Marc Manera, Martin Landriau, Mehdi Rezaie, Michael Levi, Michael Schubnell, Nathalie Palanque-Delabrouille, Peter Doel, Ramon Miquel, Santiago Avila, Satya Gontcho A Gontcho, Shadab Alam, Shaun Cole, Steven Ahlen, Theodore Kisner, Todd Claybaugh, Will Percival, Zhimin Zhou","submitted_at":"2023-07-04T14:49:23Z","abstract_excerpt":"We use angular clustering of luminous red galaxies from the Dark Energy Spectroscopic Instrument (DESI) imaging surveys to constrain the local primordial non-Gaussianity parameter $\\fnl$. Our sample comprises over 12 million targets, covering 14,000 square degrees of the sky, with redshifts in the range $0.2< z < 1.35$. We identify Galactic extinction, survey depth, and astronomical seeing as the primary sources of systematic error, and employ linear regression and artificial neural networks to alleviate non-cosmological excess clustering on large scales. Our methods are tested against simulat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.01753","kind":"arxiv","version":3},"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/2307.01753/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"}