Detailed Record



Local primordial non-Gaussianity from the large-scale clustering of photometric DESI luminous red galaxies


Abstract 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 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 $f_{\rm NL} = 34^{+24(+50)}_{-44(-73)}$ at 68 per cent(95 per cent) 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.
Authors Mehdi Rezaie ORCID , Ashley J. Ross ORCID , Hee‐Jong Seo ORCID , Hui Kong ORCID , A. Porredon ORCID , Lado Samushia ORCID , Edmond Chaussidon ORCID , Alex Krolewski ORCID , Arnaud de Mattia , Florian Beutler ORCID , Jessica Nicole Aguilar ORCID , S. P. Ahlen ORCID , Shadab Alam ORCID , S. Ávila ORCID , Benedict Bahr-Kalus ORCID , J. R. Bermejo-Climent , David Brooks , Todd Claybaugh , Shaun Cole ORCID , Kyle Dawson ORCID , Axel de la Macorra ORCID , P. Doel ORCID , Andreu Font-Ribera ORCID , Jaime E. Forero-Romero ORCID , Satya Gontcho A Gontcho ORCID , J. Guy ORCID , K. Honscheid ORCID , Dragan Huterer ORCID , T. S. Kisner ORCID , Martin Landriau ORCID , M. E. Levi ORCID , Marc Manera ORCID , Aaron M. Meisner ORCID , R. Miquel ORCID , Eva-Maria Mueller ORCID , Adam D. Myers University of Wyoming , Jeffrey A. Newman ORCID , Jundan Nie ORCID , N. Palanque‐Delabrouille ORCID , Will J. Percival ORCID , Claire Poppett , Graziano Rossi , E. Sánchez ORCID , M. Schubnell ORCID , G. Tarlé ORCID , B. A. Weaver , Christophe Yèche , Zhimin Zhou ORCID , Hu Zou ORCID
Journal Info Oxford University Press | Monthly Notices of the Royal Astronomical Society
Publication Date 5/9/2024
ISSN 0035-8711
TypeKeyword Image article
Open Access green Green Access
DOI https://doi.org/10.1093/mnras/stae886
KeywordsKeyword Image Population Mapping (Score: 0.466886)