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We investigate the large-scale clustering of the final spectroscopic sampleof quasars from the recently completed extended Baryon OscillationSpectroscopic Survey (eBOSS). The sample contains $343708$ objects in theredshift range $0.8<z<2.2$ and $72667$ objects with redshifts $2.2<z<3.5$,covering an effective area of $4699~{\rm deg}^{2}$. We develop a neuralnetwork-based approach to mitigate spurious fluctuations in the density fieldcaused by spatial variations in the quality of the imaging data used to selecttargets for follow-up spectroscopy. Simulations are used with the same angularand radial distributions as the real data to estimate covariance matrices,perform error analyses, and assess residual systematic uncertainties. Wemeasure the mean density contrast and cross-correlations of the eBOSS quasarsagainst maps of potential sources of imaging systematics to address algorithmeffectiveness, finding that the neural network-based approach outperformsstandard linear regression. Stellar density is one of the most importantsources of spurious fluctuations, and a new template constructed using datafrom the Gaia spacecraft provides the best match to the observed quasarclustering. The end-product from this work is a new value-added quasarcatalogue with the improved weights to correct for nonlinear imaging systematiceffects, which will be made public. Our quasar catalogue is used to measure thelocal-type primordial non-Gaussianity in our companion paper, Mueller et al. inpreparation.

monthly notices of the royal astronomical society

Primordial non-Gaussianity from the Completed SDSS-IV extended Baryon  Oscillation Spectroscopic Survey I: Catalogue Preparation and Systematic  Mitigation