The clustering of the SDSS-IV extended Baryon Oscillation Spectroscopic Survey quasar sample: testing observational systematics on the Baryon Acoustic Oscillation measurement

Abstract

Baryon Acoustic Oscillations are considered to be a very robust standard ruler against various systematics. This premise has been tested against observational systematics, but not to the level required for the next generation of galaxy surveys such as the Dark Energy Spectroscopic Instrument (DESI) and Euclid. In this paper, we investigate the effect of observational systematics on the BAO measurement of the final sample of quasars from the extended Baryon Oscillation Spectroscopic Survey Data Release 16 in order to prepare and hone a similar analysis for upcoming surveys. We employ catalogues with various treatments of imaging systematic effects using linear and neural network-based nonlinear approaches and consider how the BAO measurement changes. We also test how the variations to the BAO fitting model respond to the observational systematics. As expected, we confirm that the BAO measurements obtained from the DR16 quasar sample are robust against imaging systematics well within the statistical error, while reporting slightly modified constraints that shift the line-of-sight BAO signal by less than 1.1 per cent . We use realistic simulations with similar redshift and angular distributions as the DR16 sample to conduct statistical tests for validating the pipeline, quantifying the significance of differences, and estimating the expected bias on the BAO scale in future high-precision data sets. Although we find a marginal impact for the eBOSS QSO data, the work presented here is of vital importance for constraining the nature of dark energy with the BAO feature in the new era of big data cosmology.

Toward accurate measurement of property-dependent galaxy clustering: II. Tests of the smoothed density-corrected $V_{\rm max}$ method

“…Due to different systematics, it is still difficult to reliably measure small-scale property-dependent galaxy clustering at the present time. These systematics include redshiftdependent completeness, the missing galaxies in observations (Reid et al 2016;Bianchi & Percival 2017;Bianchi & Verde 2020), the incorrect estimation of the radial selection model (Ross et al 2012;Yang et al 2020), among others (Breton & de la Torre 2021; Farrow et al 2021;Merz et al 2021). Fortunately, the coming big data will considerably reduce random errors in clustering determination, but to reach the high accuracy of clustering analysis required by the next generation surveys, we must eliminate systematic errors in measurement (Beutler et al 2014;Reid et al 2016;Glanville et al 2021;Dávila-Kurbán et al 2021).…”

Toward Accurate Measurement of Property-dependent Galaxy Clustering. II. Tests of the Smoothed Density-corrected V max Method

“…Due to different systematics, it is still difficult to reliably measure small-scale property-dependent galaxy clustering at the present time. These systematics include redshift-dependent completeness, the missing galaxies in observations (Reid et al 2016;Bianchi & Percival 2017;Bianchi & Verde 2020), the incorrect estimation of the radial selection model (Ross et al 2012; Paper I), among others (Breton & de la Torre 2021;Merz et al 2021;Farrow et al 2021). Fortunately, the coming big data will considerably reduce random errors in clustering determination, but to reach the high accuracy of clustering analysis required by the next-generation surveys, we must eliminate systematic errors in measurement (Beutler et al 2014;Reid et al 2016;Glanville et al 2021;Dávila-Kurbán et al 2021).…”

The Quasar Candidates Catalogs of DESI Legacy Imaging Survey Data Release 9

“…Statistically, the spatial distribution quasars reflects the baryon acoustic oscillations (BAOs, e.g. Zhao et al 2019, for an introduction) and in turn the large-scale structure of the Universe (Dawson et al 2013;Font-Ribera et al 2014;Delubac et al 2015;Alam et al 2021;Merz et al 2021). Expectedly, with the nextgeneration large-scale surveys coming, an unprecedented dataset of QSOs brings an unparalleled opportunity to trigger a revolution in these fields.…”