期刊
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
卷 448, 期 2, 页码 1389-1401出版社
OXFORD UNIV PRESS
DOI: 10.1093/mnras/stu2754
关键词
cosmological parameters; cosmology: theory; dark energy; large-scale structure of Universe
资金
- Science and Technology Facilities Council [ST/M001946/1, ST/M001334/1] Funding Source: researchfish
- UK Space Agency [ST/K003135/1, ST/N002679/1] Funding Source: researchfish
- STFC [ST/F001991/1, ST/M001334/1, ST/M001946/1, ST/I000879/1, ST/J001511/1] Funding Source: UKRI
There is currently no consistent approach to modelling galaxy bias evolution in cosmological inference. This lack of a common standard makes the rigorous comparison or combination of probes difficult. We show that the choice of biasing model has a significant impact on cosmological parameter constraints for a survey such as the Dark Energy Survey (DES), considering the two-point correlations of galaxies in five tomographic redshift bins. We find that modelling galaxy bias with a free biasing parameter per redshift bin gives a Figure of Merit (FoM) for dark energy equation of state parameters w(0), w(a) smaller by a factor of 10 than if a constant bias is assumed. An incorrect bias model will also cause a shift in measured values of cosmological parameters. Motivated by these points and focusing on the redshift evolution of linear bias, we propose the use of a generalized galaxy bias which encompasses a range of bias models from theory, observations and simulations, b(z) = c + (b(0) - c)/D(z)(alpha), where parameters c, b(0) and a depend on galaxy properties such as halo mass. For a DES-like galaxy survey, we find that this model gives an unbiased estimate of w(0), w(a) with the same number or fewer nuisance parameters and a higher FoM than a simple b(z) model allowed to vary in z-bins. We show how the parameters of this model are correlated with cosmological parameters. We fit a range of bias models to two recent data sets, and conclude that this generalized parametrization is a sensible benchmark expression of galaxy bias on large scales.
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