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Optimized clustering estimators for BAO measurements accounting for significant redshift uncertainty

Published version
Peer-reviewed

Type

Article

Change log

Authors

Ross, Ashley J 
Banik, Nilanjan 
Avila, Santiago 
Percival, Will J 
Dodelson, Scott 

Abstract

We determine an optimized clustering statistic to be used for galaxy samples with significant redshift uncertainty, such as those that rely on photometric redshifts. To do so, we study the baryon acoustic oscillation (BAO) information content as a function of the orientation of galaxy clustering modes with respect to their angle to the line of sight (LOS). The clustering along the LOS, as observed in a redshift-space with significant redshift uncertainty, has contributions from clustering modes with a range of orientations with respect to the true LOS. For redshift uncertainty σz ≥ 0.02(1 + z), we find that while the BAO information is confined to transverse clustering modes in the true space, it is spread nearly evenly in the observed space. Thus, measuring clustering in terms of the projected separation (regardless of the LOS) is an efficient and nearly lossless compression of the signal for σz ≥ 0.02(1 + z). For reduced redshift uncertainty, a more careful consideration is required. We then use more than 1700 realizations (combining two separate sets) of galaxy simulations mimicking the Dark Energy Survey Year 1 (DES Y1) sample to validate our analytic results and optimized analysis procedure. We find that using the correlation function binned in projected separation, we can achieve uncertainties that are within 10 per cent of those predicted by Fisher matrix forecasts. We predict that DES Y1 should achieve a 5 per cent distance measurement using our optimized methods. We expect the results presented here to be important for any future BAO measurements made using photometric redshift data.

Description

Keywords

distance scale, large-scale structure of Universe

Journal Title

Monthly Notices of the Royal Astronomical Society

Conference Name

Journal ISSN

0035-8711
1365-2966

Volume Title

472

Publisher

Oxford University Press
Sponsorship
Science and Technology Facilities Council (ST/L000636/1)
Science and Technology Facilities Council (ST/P000673/1)
Please visit publisher's website for further information.