@article {647965,
	title = {Tests of acoustic scale shifts in halo-based mock galaxy catalogues},
	journal = {Monthly Notices of the Royal Astronomical Society},
	volume = {490},
	year = {2019},
	month = {December 01, 201},
	pages = {2718-2731},
	abstract = {We utilize mock catalogues from high-accuracy cosmological N-bodysimulations to quantify shifts in the recovery of the acoustic scalethat could potentially result from galaxy clustering bias. Therelationship between galaxies and dark matter haloes presents acomplicated source of systematic errors in modern redshift surveys,particularly when aiming to make cosmological measurements to sub-percent precision. Apart from a scalar, linear bias parameter accountingfor the density contrast ratio between matter tracers and the truematter distribution, other types of galaxy bias, such as assembly andvelocity biases, may also significantly alter clustering signals fromsmall to large scales. We create mocks based on generalized halooccupation populations of 36 periodic boxes from the ABACUSCOSMOSrelease, and test various biased models along with an unbiasedbase case in a total volume of 48 h^{-3} Gpc3. Tworeconstruction methods are applied to galaxy samples and the apparentacoustic scale is derived by fitting the two-point correlation functionmultipoles. With respect to the baseline, we find a 0.3 per cent shiftin the line-of-sight acoustic scale for one variation in the satellitegalaxy population, and we find a 0.7 per cent shift for an extreme levelof velocity bias of the central galaxies. All other bias models areconsistent with zero shift at the 0.2 per cent level afterreconstruction. We note that the bias models explored are relativelylarge variations, producing sizeable and likely distinguishable changesin small-scale clustering, the modelling of which would furthercalibrate the baryon acoustic oscillations standard ruler.},
	keywords = {Cosmology and Nongalactic Astrophysics},
	isbn = {0035-8711},
	url = {https://ui.adsabs.harvard.edu/abs/2019MNRAS.490.2718D},
	author = {Duan, Yutong and Eisenstein, Daniel}
}
