Local primordial non-Gaussianity from the large-scale clustering of
  photometric DESI luminous red galaxies
Mehdi Rezaie Ashley J. Ross Hee-Jong Seo Hui Kong Anna Porredon Lado Samushia Edmond Chaussidon Alex Krolewski Arnaud de Mattia Florian Beutler Jessica Nicole Aguilar Steven Ahlen Shadab Alam Santiago Avila Benedict Bahr-Kalus Jose Bermejo-Climent David Brooks Todd Claybaugh Shaun Cole Kyle Dawson Axel de la Macorra Peter Doel Andreu Font-Ribera Jaime E. Forero-Romero Satya Gontcho A Gontcho Julien Guy Klaus Honscheid Theodore Kisner Martin Landriau Michael Levi Marc Manera Aaron Meisner Ramon Miquel Eva-Maria Mueller Adam Myers Jeffrey A. Newman Jundan Nie Nathalie Palanque-Delabrouille Will Percival Claire Poppett Graziano Rossi Eusebio Sanchez Michael Schubnell Gregory Tarlé Benjamin Alan Weaver Christophe Yèche Zhimin Zhou Hu Zou
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 log-normal simulations with and
without fNL and systematics, showing superior performance of the neural network
treatment in reducing remaining systematics. Assuming the universality
relation, we find fNL $= 47^{+14(+29)}_{-11(-22)}$ at 68\%(95\%) confidence.
With a more aggressive treatment, including regression against the full set of
imaging maps, our maximum likelihood value shifts slightly to fNL$ \sim 50$ and
the uncertainty on fNL increases due to the removal of large-scale clustering
information. We apply a series of robustness tests (e.g., cuts on imaging,
declination, or scales used) that show consistency in the obtained constraints.
Despite extensive efforts to mitigate systematics, our measurements indicate
fNL > 0 with a 99.9 percent confidence level. This outcome raises concerns as
it could be attributed to unforeseen systematics, including calibration errors
or uncertainties associated with low-\ell systematics in the extinction
template. Alternatively, it could suggest a scale-dependent fNL model--causing
significant non-Gaussianity around large-scale structure while leaving cosmic
microwave background scales unaffected. Our results encourage further studies
of fNL with DESI spectroscopic samples, where the inclusion of 3D clustering
modes should help separate imaging systematics.