Improving baryon acoustic oscillation measurement with the combination of cosmic voids and galaxies. Issue 3 (28th November 2019)

• Record Type: Journal Article
• Authors: Zhao, Cheng; Chuang, Chia-Hsun; Kitaura, Francisco-Shu; Liang, Yu; Pellejero-Ibanez, Marcos; Tao, Charling; Vargas-Magaña, Mariana; Variu, Andrei; Yepes, Gustavo
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 491:Issue 3(2020)
• Page Start: 4554
• Page End: 4572
• Publication Date: 2019-11-28
• Subjects: methods: data analysis -- methods: statistical -- galaxies: statistics -- cosmology: observations -- large-scale structure of Universe; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/stz3339
• Languages: English
• ISSNs: 0035-8711

Abstract

ABSTRACT: We develop a methodology to optimize the measurement of baryon acoustic oscillation (BAO) from a given galaxy sample. In our previous work, we demonstrated that one can measure BAO from tracers in underdense regions (voids). In this study, we combine the overdense and underdense tracers (galaxies and voids) to obtain better constraints on the BAO scale. To this end, we modify the de-wiggled BAO model with an additional parameter to describe both the BAO peak and the underlying exclusion pattern of void 2PCFs. We show that after applying BAO reconstruction to galaxies, the BAO peak scale of both galaxies and voids are unbiased using the modified model. Furthermore, we use a new 2PCF estimator for a multitracer analysis with galaxies and voids. In simulations, the joint sample improves by about 10 per cent the constraint for the post-reconstruction BAO peak position compared to the result from galaxies alone, which is equivalent to an enlargement of the survey volume by 20 per cent. Applying this method to the BOSS DR12 data, we have an 18 per cent improvement for the low- z sample (0.2 < z < 0.5), but a worse constraint for the high- z sample (0.5 < z < 0.75), which is consistent with statistical fluctuations for the current survey volume. Future larger samples will give more robust improvements due to less statistical fluctuations.