A Caltech Library Service

Linear bias forecasts for emission line cosmological surveys

Merson, Alexander and Smith, Alex and Benson, Andrew and Wang, Yun and Baugh, Carlton (2019) Linear bias forecasts for emission line cosmological surveys. Monthly Notices of the Royal Astronomical Society, 486 (4). pp. 5737-5765. ISSN 0035-8711. doi:10.1093/mnras/stz1204.

[img] PDF - Published Version
See Usage Policy.

[img] PDF - Accepted Version
See Usage Policy.


Use this Persistent URL to link to this item:


We forecast the linear bias for  Hα-emitting galaxies at high redshift. To simulate a Euclid-like and a WFIRST-like survey, we place galaxies into a large-volume dark matter halo lightcone by sampling a library of luminosity-dependent halo occupation distributions (HODs), which is constructed using a physically motivated galaxy formation model. We calibrate the dust attenuation in the lightcones such that they are able to reproduce the  Hα luminosity function or the  Hα cumulative number counts. The angle-averaged galaxy correlation function is computed for each survey in redshift slices of width Δz = 0.2. In each redshift bin the linear bias can be fitted with a single, scale-independent value that increases with increasing redshift. Fitting for the evolution of linear bias with redshift, we find that our Euclid-like and WFIRST-like surveys are both consistent within error with the relation b(z) = 0.7z + 0.7. Our bias forecasts are consistent with bias measurements from the HiZELS survey. We find that the Euclid-like and WFIRST-like surveys yield linear biases that are broadly consistent within error, most likely due to the HOD for the WFIRST-like survey having a steeper power-law slope towards larger halo masses.

Item Type:Article
Related URLs:
URLURL TypeDescription Paper
Benson, Andrew0000-0001-5501-6008
Baugh, Carlton0000-0002-9935-9755
Additional Information:© 2019 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 ( Accepted 2019 April 18. Received 2019 April 17; in original form 2019 February 28. Published: 06 May 2019. We thank the anonymous referee for their many helpful comments and suggestions that greatly enhanced the discussion of this work. We additionally thank Anahita Alvari, Iary Davidzon, Andreas Faisst, Zhongxu Zhai, and the members of the JPL Darksector group for various insightful conversations that helped improve this work. AM acknowledges sponsorship of a NASA Postdoctoral Program Fellowship. AM was supported by JPL, which is run under contract by California Institute of Technology for NASA. This work was supported by NASA ROSES grant 12-EUCLID12-0004 and by NASA grant 15-WFIRST15-0008 ‘Cosmology with the High Latitude Survey’ WFIRST Science Investigation Team (SIT). This work used the DiRAC@Durham facility managed by the Institute for Computational Cosmology on behalf of the STFC DiRAC HPC Facility ( The equipment was funded by BEIS capital funding via STFC capital grants ST/K00042X/1, ST/P002293/1, ST/R002371/1, and ST/S002502/1, Durham University, and STFC operations grant ST/R000832/1. DiRAC is part of the National e-Infrastructure.
Funding AgencyGrant Number
NASA Postdoctoral ProgramUNSPECIFIED
Science and Technology Facilities Council (STFC)ST/K00042X/1
Science and Technology Facilities Council (STFC)ST/P002293/1
Science and Technology Facilities Council (STFC)ST/R002371/1
Science and Technology Facilities Council (STFC)ST/S002502/1
Durham UniversityUNSPECIFIED
Science and Technology Facilities Council (STFC)ST/R000832/1
Subject Keywords:methods: numerical, galaxies: formation, galaxies: statistics, large-scale structure of Universe
Issue or Number:4
Record Number:CaltechAUTHORS:20190801-090235301
Persistent URL:
Official Citation:Alexander Merson, Alex Smith, Andrew Benson, Yun Wang, Carlton Baugh, Linear bias forecasts for emission line cosmological surveys, Monthly Notices of the Royal Astronomical Society, Volume 486, Issue 4, July 2019, Pages 5737–5765,
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:97576
Deposited By: Tony Diaz
Deposited On:01 Aug 2019 16:18
Last Modified:16 Nov 2021 17:32

Repository Staff Only: item control page