Reconstructing the baryon acoustic oscillations using biased tracers
- Creators
- Birkin, Jack
- Li, Baojiu
- Cautun, Marius
- Shi, Yanlong
Abstract
The reconstruction of the initial conditions of the Universe is an important topic in cosmology, particularly in the context of sharpening the measurement of the baryon acoustic oscillation (BAO) peak. Non-linear reconstruction algorithms developed in recent years, when applied to late-time matter fields, can recover to a substantial degree the initial density distribution, however, when applied to sparse tracers of the matter field, the performance is poorer. In this paper, we apply the Shi et al. non-linear reconstruction method to biased tracers in order to establish what factors affect the reconstruction performance. We find that grid resolution, tracer number density, and mass assignment scheme all have a significant impact on the performance of our reconstruction method, with triangular-shaped-cloud mass assignment and a grid resolution of ∼1-2h^(−1) Mpc being the optimal choice. We also show that our method can be easily adapted to include generic tracer biases up to quadratic order in the reconstruction formalism. Applying the reconstruction to halo and galaxy samples with a range of tracer number densities, we find that the linear bias is by far the most important bias term, while including non-local and non-linear biases only leads to marginal improvements on the reconstruction performance. Overall, including bias in the reconstruction substantially improves the recovery of BAO wiggles, down to k∼0.25h Mpc^(−1) for tracer number densities between 2 × 10^(−4) and 2×10^(−3).
Additional Information
© 2018 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model). Accepted 2018 December 4. Received 2018 November 28; in original form 2018 September 29. Published: 11 December 2018. We thank Xin Wang and Hong-Ming Zhu for helpful discussions during this project and the anonymous referee for their insightful comments. JB and BL are supported by the European Research Council (ERC-StG-716532-PUNCA), and BL and MC are supported by the STFC through grant ST/P000541/1. This work used the DiRAC Data Centric system at Durham University, operated by the Institute for Computational Cosmology on behalf of the STFC DiRAC HPC Facility (www.dirac.ac.uk). This equipment was funded by BIS National E-infrastructure capital grant ST/K00042X/1, STFC capital grant ST/H008519/1, and STFC DiRAC Operations grant ST/K003267/1 and Durham University. DiRAC is part of the National E-Infrastructure.Attached Files
Published - sty3365.pdf
Accepted Version - 1809.08135.pdf
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Additional details
- Eprint ID
- 94672
- Resolver ID
- CaltechAUTHORS:20190411-154102834
- 716532
- European Research Council (ERC)
- ST/P000541/1
- Science and Technology Facilities Council (STFC)
- ST/K00042X/1
- Science and Technology Facilities Council (STFC)
- ST/H008519/1
- Science and Technology Facilities Council (STFC)
- ST/K003267/1
- Science and Technology Facilities Council (STFC)
- Durham University
- Created
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2019-04-12Created from EPrint's datestamp field
- Updated
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2021-11-16Created from EPrint's last_modified field