Open AccessCosmological constraints from the cosmic microwave background and Lyman α forest revisited
Author(s) -
Seljak Uroš,
McDonald Patrick,
Makarov Alexey
Publication year - 2003
Publication title -
monthly notices of the royal astronomical society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.058
H-Index - 383
eISSN - 1365-2966
pISSN - 0035-8711
DOI - 10.1046/j.1365-8711.2003.06809.x
Subject(s) - physics , cosmic microwave background , cmb cold spot , spectral density , astrophysics , degeneracy (biology) , cosmic background radiation , amplitude , cosmic cancer database , cold dark matter , statistical physics , cosmology , statistics , quantum mechanics , bioinformatics , anisotropy , mathematics , biology
The WMAP team has recently highlighted the usefulness of combining the Lyα forest constraints with those from the cosmic microwave background (CMB). This combination is particularly powerful as a probe of the primordial shape of the power spectrum. Converting between the Lyα forest observations and the linear mass power spectrum requires a careful treatment of nuisance parameters and modelling with cosmological simulations. We point out several issues which lead to an expansion of the errors, the two most important being the range of cosmological parameters explored in simulations and the treatment of the mean transmitted flux constraints. We employ a likelihood calculator for the current Lyα data set based on an extensive six‐dimensional grid of simulations. We show that the current uncertainties in the mean transmission and the flux power spectrum define a degeneracy line in the amplitude–slope plane. The CMB degeneracy owing to the primordial power spectrum shape follows a similar relation in this plane. This weakens the statistical significance of the primordial power spectrum shape constraints based on combined CMB+Lyα forest analysis. Using the current data, the simplest n = 1 scale‐invariant model with d n / d ln k = 0 and no tensors has a Δχ 2 = 4 compared with the best‐fitting model in which these three parameters are free. Current data therefore do not require relaxing these parameters to improve the fit.