The Cosmic Microwave Background and the Ionization History of the Universe

Details of how the primordial plasma recombined and how the universe later reionized are currently somewhat uncertain. This uncertainty can restrict the accuracy of cosmological parameter measurements from the Cosmic Microwave Background (CMB). More positively, future CMB data can be used to constrain the ionization history using observations. We first discuss how current uncertainties in the recombination history impact parameter constraints, and show how suitable parameterizations can be used to obtain unbiased parameter estimates from future data. Some parameters can be constrained robustly, however there is clear motivation to model recombination more accurately with quantified errors. We then discuss constraints on the ionization fraction binned in redshift during reionization. Perfect CMB polarization data could in principle distinguish different histories that have the same optical depth. We discuss how well the Planck satellite may be able to constrain the ionization history, and show the currently very weak constraints from WMAP three-year data.Comment: Changes to match MNRAS accepted versio

Small-scale CMB Temperature and Polarization Anisotropies due to Patchy Reionization

We study contributions from inhomogeneous (patchy) reionization to arcminute scale ($1000 < \ell < 10,000$) cosmic microwave background (CMB) anisotropies. We show that inhomogeneities in the ionization fraction, rather than in the mean density, dominate both the temperature and the polarization power spectra. Depending on the ionization history and the clustering bias of the ionizing sources, we find that rms temperature fluctuations range from 2 $\mu$K to 8 $\mu$K and the corresponding values for polarization are over two orders of magnitude smaller. Reionization can significantly bias cosmological parameter estimates and degrade gravitational lensing potential reconstruction from temperature maps but not from polarization maps. We demonstrate that a simple modeling of the reionization temperature power spectrum may be sufficient to remove the parameter bias. The high-$\ell$ temperature power spectrum will contain some limited information about the sources of reionization.Comment: 11 pages, 8 figures. Minor changes to match version accepted by Ap

Tomography of the Reionization Epoch with Multifrequency CMB Observations

We study the constraints that future multifrequency Cosmic Microwave Background (CMB) experiments will be able to set on the metal enrichment history of the Inter Galactic Medium at the epoch of reionisation. We forecast the signal to noise ratio for the detection of the signal introduced in the CMB by resonant scattering off metals at the end of the Dark Ages. We take into account systematics associated to inter-channel calibration, PSF reconstruction errors and innacurate foreground removal. We develop an algorithm to optimally extract the signal generated by metals during reionisation and to remove accurately the contamination due to the thermal Sunyaev-Zel'dovich effect. Although demanding levels of foreground characterisation and control of systematics are required, they are very distinct from those encountered in HI-21cm studies and CMB polarization, and this fact encourages the study of resonant scattering off metals as an alternative way of conducting tomography of the reionisation epoch. An ACT-like experiment with optimistic assumtions on systematic effects, and looking at clean regions of the sky, can detect changes of 3%-12% (95% c.l.) of the OIII abundance (with respect its solar value) in the redshift range $z\in$ [12,22], for reionization redshift $z_{\rm re}>10$. However, for $z_{\rm re} <10$, it can only set upper limits on NII abundance increments of $\sim$ 60% its solar value in the redshift range $z\in$ [5.5,9], (95% c.l.). These constraints assume that inter-channel calibration is accurate down to one part in $10^{4}$, which constitutes the most critical technical requirement of this method, but still achievable with current technology.Comment: 10 pages, 2 figures, submitted to Astrophysical Journal. Comments are welcom

Higher-Order Gravitational Lensing Reconstruction using Feynman Diagrams

We develop a method for calculating the correlation structure of the Cosmic Microwave Background (CMB) using Feynman diagrams, when the CMB has been modified by gravitational lensing, Faraday rotation, patchy reionization, or other distorting effects. This method is used to calculate the bias of the Hu-Okamoto quadratic estimator in reconstructing the lensing power spectrum up to O(\phi^4) in the lensing potential $\phi$. We consider both the diagonal noise TTTT, EBEB, etc. and, for the first time, the off-diagonal noise TTTE, TBEB, etc. The previously noted large O(\phi^4) term in the second order noise is identified to come from a particular class of diagrams. It can be significantly reduced by a reorganization of the $\phi$ expansion. These improved estimators have almost no bias for the off-diagonal case involving only one $B$ component of the CMB, such as EEEB.Comment: 17 pages, 17 figure

An Emulator for the Lyman-alpha Forest

We present methods for interpolating between the 1-D flux power spectrum of the Lyman-$\alpha$ forest, as output by cosmological hydrodynamic simulations. Interpolation is necessary for cosmological parameter estimation due to the limited number of simulations possible. We construct an emulator for the Lyman-$\alpha$ forest flux power spectrum from $21$ small simulations using Latin hypercube sampling and Gaussian process interpolation. We show that this emulator has a typical accuracy of 1.5% and a worst-case accuracy of 4%, which compares well to the current statistical error of 3 - 5% at $z < 3$ from BOSS DR9. We compare to the previous state of the art, quadratic polynomial interpolation. The Latin hypercube samples the entire volume of parameter space, while quadratic polynomial emulation samples only lower-dimensional subspaces. The Gaussian process provides an estimate of the emulation error and we show using test simulations that this estimate is reasonable. We construct a likelihood function and use it to show that the posterior constraints generated using the emulator are unbiased. We show that our Gaussian process emulator has lower emulation error than quadratic polynomial interpolation and thus produces tighter posterior confidence intervals, which will be essential for future Lyman-$\alpha$ surveys such as DESI.Comment: 28 pages, 10 figures, accepted to JCAP with minor change