Cookies

We use cookies to ensure that we give you the best experience on our website. By continuing to browse this repository, you give consent for essential cookies to be used. You can read more about our Privacy and Cookie Policy.


Durham Research Online
You are in:

Marked correlation functions in perturbation theory.

Aviles, Alejandro and Koyama, Kazuya and Cervantes-Cota, Jorge L. and Winther, Hans A. and Li, Baojiu (2020) 'Marked correlation functions in perturbation theory.', Journal of cosmology and astroparticle physics., 01 . 006.

Abstract

We develop perturbation theory approaches to model the marked correlation function constructed to up-weight low density regions of the Universe, which might help distinguish modified gravity models from the standard cosmology model based on general relativity. Working within Convolution Lagrangian Perturbation Theory, we obtain that weighted correlation functions are expressible as double convolution integrals that we approximate using a combination of Eulerian and Lagrangian schemes. We find that different approaches agree within 1% on quasi non-linear scales. Compared with N-body simulations, the perturbation theory is found to provide accurate predictions for the marked correlation function of dark matter fields, dark matter halos as well as Halo Occupation Distribution galaxies down to 30 Mpc/h. These analytic approaches help to understand the degeneracy between the mark and the galaxy bias and find a way to maximize the differences among various cosmological models.

Item Type:Article
Full text:(AM) Accepted Manuscript
Available under License - Creative Commons Attribution Non-commercial No Derivatives.
Download PDF
(1701Kb)
Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1088/1475-7516/2020/01/006
Publisher statement:The deposited manuscript is available under a CC BY-NC-ND 3.0 licence.
Date accepted:10 December 2019
Date deposited:10 January 2020
Date of first online publication:02 January 2020
Date first made open access:02 January 2021

Save or Share this output

Export:
Export
Look up in GoogleScholar