Impacts of Dark Stars on Reionization and Signatures in the Cosmic Microwave Background

Details Impacts of Dark Stars on Reionization and Signatures in the Cosmic Microwave Background Impacts of Dark Stars on Reionization and Signatures in the Cosmic Microwave Background

2011-07-08

arxiv.org/abs/1107.1714v2

ApJ 742 (2011) 129

Astronomy and Astrophysics

Astrophysics

Cosmology and Extragalactic Astrophysics

Small changes in response to referee's comments; matches version accepted for publication in ApJ. 14 pages, 9 figures

Scientific paper

10.1088/0004-637X/742/2/129

We perform a detailed and systematic investigation of the possible impacts of dark stars upon the reionization history of the Universe, and its signatures in the cosmic microwave background (CMB). We compute hydrogen reionization histories, CMB optical depths and anisotropy power spectra for a range of stellar populations including dark stars. If dark stars capture large amounts of dark matter via nuclear scattering, reionization can be substantially delayed, leading to decreases in the integrated optical depth to last scattering and large-scale power in the EE polarization power spectrum. Using the integrated optical depth observed by WMAP7, in our canonical reionization model we rule out the section of parameter space where dark stars with high scattering-induced capture rates tie up more than ~90% of all the first star-forming baryons, and live for over ~250 Myr. When nuclear scattering delivers only moderate amounts of dark matter, reionization can instead be sped up slightly, modestly increasing the CMB optical depth. If dark stars do not obtain any dark matter via nuclear scattering, effects upon reionization and the CMB are negligible. The effects of dark stars upon reionization and its CMB markers can be largely mimicked or compensated for by changes in the existing parameters of reionization models, making dark stars difficult to disentangle from astrophysical uncertainties, but also widening the range of standard parameters in reionization models that can be made consistent with observations.

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