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Register a new userJoint estimation of the Epoch of Reionization power spectrum and foregrounds
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The power spectrum of redshifted 21 cm emission brightness temperature fluctuations is a powerful probe of the Epoch of Reionization (EoR). However, bright foreground emission presents a significant impediment to its unbiased recovery from interferometric data. We build on the Bayesian power spectral estimation methodology introduced in Sims et al. 2016 and demonstrate that incorporating a priori knowledge of the spectral structure of foregrounds in the large spectral scale component of the data model enables significantly improved modelling of the foregrounds without increasing the model complexity. We explore two astrophysically motivated parametrisations of the large spectral scale model: (i) a constant plus power law model of the form $q_{0}+q_{1}( u/ u_{0})^{b_{1}}$ for two values of $b_{1}$: $b_{1} = <beta>_mathrm{GDSE}$ and $b_{1} = <beta>_mathrm{EGS}$, the mean spectral indices of the Galactic diffuse synchrotron emission and extragalactic source foreground emission, respectively, and (ii) a constant plus double power law model of the form $q_{0}+q_{1}( u/ u_{0})^{b_{1}}+q_{2}( u/ u_{0})^{b_{2}}$ with $b_{1} = <beta>_mathrm{GDSE}$ and $b_{2} = <beta>_mathrm{EGS}$. We estimate the EoR power spectrum from simulated interferometric data consisting of an EoR signal, Galactic diffuse synchrotron emission, extragalactic sources and diffuse free-free emission from the Galaxy. We show that, by jointly estimating a model of the EoR signal with the constant plus double power law parametrisation of the large spectral scale model, unbiased estimates of the EoR power spectrum are recoverable on all spatial scales accessible in the data set, including on the large spatial scales that were found to be contaminated in earlier work.
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We construct foreground simulations comprising spatially correlated extragalactic and diffuse Galactic emission components and calculate the `intrinsic (instrument-free) two-dimensional spatial power spectrum and the cylindrically and spherically averaged three-dimensional k-space power spectra of the Epoch of Reionization (EoR) and our foreground simulations using a Bayesian power spectral estimation framework. This leads us to identify a model dependent region of optimal signal estimation for our foreground and EoR models, within which the spatial power in the EoR signal relative to foregrounds is maximised. We identify a target field dependent region, in k-space, of intrinsic foreground power spectral contamination at low k_perp and k_parallel and a transition to a relatively foreground-free intrinsic EoR window in the complement to this region. The contaminated region of k-space demonstrates that simultaneous estimation of the EoR and foregrounds is important for obtaining statistically robust estimates of the EoR power spectrum; biased results will be obtained from methodologies that ignore their covariance. Using simulated observations with frequency dependent uv-coverage and primary beam, with the former derived for HERA in 37-antenna and 331-antenna configuration, we recover instrumental power spectra consistent with their intrinsic counterparts. We discuss the implications of these results for optimal strategies for unbiased estimation of the EoR power spectrum.
We introduce a new method for performing robust Bayesian estimation of the three-dimensional spatial power spectrum at the Epoch of Reionization (EoR), from interferometric observations. The versatility of this technique allows us to present two approaches. First, when the observations span only a small number of independent spatial frequencies ($k$-modes) we sample directly from the spherical power spectrum coefficients that describe the EoR signal realisation. Second, when the number of $k$-modes to be included in the model becomes large, we sample from the joint probability density of the spherical power spectrum and the signal coefficients, using Hamiltonian Monte Carlo methods to explore this high dimensional ($sim$ 20000) space efficiently. This approach has been successfully applied to simulated observations that include astrophysically realistic foregrounds in a companion publication (Sims et al. 2016). Here we focus on explaining the methodology in detail, and use simple foreground models to both demonstrate its efficacy, and highlight salient features. In particular, we show that including an arbitrary flat spectrum continuum foreground that is $10^8$ times greater in power than the EoR signal has no detectable impact on our parameter estimates of the EoR power spectrum recovered from the data.
Intensity mapping of the HI 21 cm line and the CO 2.61 mm line from the epoch of reionization has emerged as powerful, complementary, probes of the high-redshift Universe. However, both maps and their cross-correlation are dominated by foregrounds. We propose a new analysis by which the signal is unbiased by foregrounds, i.e. it can be measured without foreground mitigation. We construct the antisymmetric part of the HI-CO cross-correlation, arising because the statistical fluctuations of two fields have different evolution in time. We show that the sign of this new signal can distinguish model-independently whether inside-out reionization happens during some interval of time.
The Detection of redshifted 21 cm emission from the epoch of reionization (EoR) is a challenging task owing to strong foregrounds that dominate the signal. In this paper, we propose a general method, based on the delay spectrum approach, to extract HI power spectra that is applicable to tracking observations using an imaging radio interferometer (Delay Spectrum with Imaging Arrays (DSIA)). Our method is based on modelling the HI signal taking into account the impact of wide field effects such as the $w$-term which are then used as appropriate weights in cross-correlating the measured visibilities. Our method is applicable to any radio interferometer that tracks a phase center and could be utilized for arrays such as MWA, LOFAR, GMRT, PAPER and HERA. In the literature the delay spectrum approach has been implemented for near-redundant baselines using drift scan observations. In this paper we explore the scheme for non-redundant tracking arrays, and this is the first application of delay spectrum methodology to such data to extract the HI signal. We analyze 3 hours of MWA tracking data on the EoR1 field. We present both 2-dimensional ($k_parallel,k_perp$) and 1-dimensional (k) power spectra from the analysis. Our results are in agreement with the findings of other pipelines developed to analyse the MWA EoR data.
The non-Gaussian nature of the epoch of reionization (EoR) 21-cm signal has a significant impact on the error variance of its power spectrum $P({bf textit{k}})$. We have used a large ensemble of semi-numerical simulations and an analytical model to estimate the effect of this non-Gaussianity on the entire error-covariance matrix ${mathcal{C}}_{ij}$. Our analytical model shows that ${mathcal{C}}_{ij}$ has contributions from two sources. One is the usual variance for a Gaussian random field which scales inversely of the number of modes that goes into the estimation of $P({bf textit{k}})$. The other is the trispectrum of the signal. Using the simulated 21-cm signal ensemble, an ensemble of the randomized signal and ensembles of Gaussian random ensembles we have quantified the effect of the trispectrum on the error variance ${mathcal{C}}_{ij}$. We find that its relative contribution is comparable to or larger than that of the Gaussian term for the $k$ range $0.3 leq k leq 1.0 ,{rm Mpc}^{-1}$, and can be even $sim 200$ times larger at $k sim 5, {rm Mpc}^{-1}$. We also establish that the off-diagonal terms of ${mathcal{C}}_{ij}$ have statistically significant non-zero values which arise purely from the trispectrum. This further signifies that the error in different $k$ modes are not independent. We find a strong correlation between the errors at large $k$ values ($ge 0.5 ,{rm Mpc}^{-1}$), and a weak correlation between the smallest and largest $k$ values. There is also a small anti-correlation between the errors in the smallest and intermediate $k$ values. These results are relevant for the $k$ range that will be probed by the current and upcoming EoR 21-cm experiments.
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