Primordial non-Gaussianity from the Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey I: Catalogue Preparation and Systematic Mitigation
Mehdi Rezaie, Ashley J. Ross, Hee-Jong Seo, Eva-Maria Mueller, Will J., Percival, Grant Merz, Reza Katebi, Razvan C. Bunescu, Julian Bautista, Joel, R. Brownstein, Etienne Burtin, Kyle Dawson, H\'ector Gil-Mar\'in, Jiamin Hou,, Eleanor B. Lyke, Axel de la Macorra, Graziano Rossi

TL;DR
This paper presents a new quasar catalog from eBOSS with improved systematic mitigation techniques, enabling more accurate measurements of primordial non-Gaussianity in the universe.
Contribution
We develop a neural network-based method to mitigate imaging systematics in quasar clustering data, improving the accuracy of cosmological measurements.
Findings
Neural network approach outperforms linear regression in systematic mitigation.
A Gaia-based stellar density template effectively reduces spurious fluctuations.
The resulting catalog enhances the precision of primordial non-Gaussianity constraints.
Abstract
We investigate the large-scale clustering of the final spectroscopic sample of quasars from the recently completed extended Baryon Oscillation Spectroscopic Survey (eBOSS). The sample contains objects in the redshift range and objects with redshifts , covering an effective area of . We develop a neural network-based approach to mitigate spurious fluctuations in the density field caused by spatial variations in the quality of the imaging data used to select targets for follow-up spectroscopy. Simulations are used with the same angular and radial distributions as the real data to estimate covariance matrices, perform error analyses, and assess residual systematic uncertainties. We measure the mean density contrast and cross-correlations of the eBOSS quasars against maps of potential sources of imaging systematics to address…
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