Local primordial non-Gaussianity from the large-scale clustering of photometric DESI luminous red galaxies
Creators: name: Rezaie, Mehdi
name: Ross, Ashley J.
name: Seo, Hee-Jong
name: Kong, Hui
name: Porredon, Anna
name: Samushia, Lado
name: Chaussidon, Edmond
name: Krolewski, Alex
name: de Mattia, Arnaud
name: Beutler, Florian
name: Aguilar, Jessica Nicole
name: Ahlen, Steven
name: Alam, Shadab
name: Avila, Santiago
name: Bahr-Kalus, Benedict
name: Bermejo-Climent, Jose
name: Brooks, David
name: Claybaugh, Todd
name: Cole, Shaun
name: Dawson, Kyle
name: de la Macorra, Axel
name: Doel, Peter
name: Font-Ribera, Andreu
name: Forero-Romero, Jaime E.
name: Gontcho, Satya Gontcho A
name: Guy, Julien
name: Honscheid, Klaus
name: Huterer, Dragan
name: Kisner, Theodore
name: Landriau, Martin
name: Levi, Michael
name: Manera, Marc
name: Meisner, Aaron
name: Miquel, Ramon
name: Mueller, Eva-Maria
name: Myers, Adam
name: Newman, Jeffrey A.
name: Nie, Jundan
name: Palanque-Delabrouille, Nathalie
name: Percival, Will
name: Poppett, Claire
name: Rossi, Graziano
name: Sanchez, Eusebio
name: Schubnell, Michael
name: Tarlé, Gregory
name: Weaver, Benjamin Alan
name: Yèche, Christophe
name: Zhou, Zhimin
name: Zou, Hu
Publisher: name: arXiv
Year: 2023
Subjects: subject: Cosmology and Nongalactic Astrophysics (astro-ph.CO)
subject: Machine Learning (cs.LG)
subject: Computational Physics (physics.comp-ph)
subject: Data Analysis, Statistics and Probability (physics.data-an)
subject: FOS: Physical sciences
subject: FOS: Physical sciences
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences

description: 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 $0.2&lt; z &lt; 1.35$. 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 simulations with and without $\fnl$ and systematics, showing superior performance of the neural network treatment. The neural network with a set of nine imaging property maps passes our systematic null test criteria, and is chosen as the fiducial treatment. Assuming the universality relation, we find $\fnl = 34^{+24(+50)}_{-44(-73)}$ at 68\%(95\%) confidence. We apply a series of robustness tests (e.g., cuts on imaging, declination, or scales used) that show consistency in the obtained constraints. We study how the regression method biases the measured angular power-spectrum and degrades the $\fnl$ constraining power. The use of the nine maps more than doubles the uncertainty compared to using only the three primary maps in the regression. Our results thus motivate the development of more efficient methods that avoid over-correction, protect large-scale clustering information, and preserve constraining power. Additionally, our results encourage further studies of $\fnl$ with DESI spectroscopic samples, where the inclusion of 3D clustering modes should help separate imaging systematics and lessen the degradation in the $\fnl$ uncertainty.
description: 21 pages, 17 figures, 7 tables (Appendix excluded). Published in MNRAS