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

TL;DR

This study assesses how observational systematics affect BAO measurements in quasar samples, confirming robustness within current errors and preparing methods for future surveys like DESI and Euclid.

Contribution

It introduces a comprehensive analysis of observational systematics on BAO measurements using advanced modeling and simulations, tailored for upcoming large-scale surveys.

Findings

BAO measurements are robust against imaging systematics within statistical errors.

Line-of-sight BAO signal shifts are less than 1.1%.

Simulations validate the pipeline and estimate biases for future data.

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% . 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 with DESI and Euclid.