Journal
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
Volume 454, Issue 2, Pages 1958-1975Publisher
OXFORD UNIV PRESS
DOI: 10.1093/mnras/stv2036
Keywords
gravitational lensing: weak; cosmology: theory; dark energy; large-scale structure of Universe
Categories
Funding
- STFC
- European Research Council under the EC FP7 grant [240185]
- STFC [ST/J001422/1, ST/K001051/1] Funding Source: UKRI
- Science and Technology Facilities Council [ST/K001051/1] Funding Source: researchfish
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We present an optimized variant of the halo model, designed to produce accurate matter power spectra well into the non-linear regime for a wide range of cosmological models. To do this, we introduce physically motivated free parameters into the halo-model formalism and fit these to data from high-resolution N-body simulations. For a variety of Lambda cold dark matter (Lambda CDM) and wCDM models, the halo-model power is accurate to similar or equal to 5 per cent for k <= 10h Mpc(-1) and z <= 2. An advantage of our new halo model is that it can be adapted to account for the effects of baryonic feedback on the power spectrum. We demonstrate this by fitting the halo model to power spectra from the OWLS (OverWhelmingly Large Simulations) hydrodynamical simulation suite via parameters that govern halo internal structure. We are able to fit all feedback models investigated at the 5 per cent level using only two free parameters, and we place limits on the range of these halo parameters for feedback models investigated by the OWLS simulations. Accurate predictions to high k are vital for weak-lensing surveys, and these halo parameters could be considered nuisance parameters to marginalize over in future analyses to mitigate uncertainty regarding the details of feedback. Finally, we investigate how lensing observables predicted by our model compare to those from simulations and from HALOFIT for a range of k-cuts and feedback models and quantify the angular scales at which these effects become important. Code to calculate power spectra from the model presented in this paper can be found at https://github.com/alexander-mead/hmcode.
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