The impact of spin temperature fluctuations on the 21-cm moments

This paper considers the impact of Lyman-alpha coupling and X-ray heating on the 21-cm brightness-temperature one-point statistics (as predicted by semi-numerical simulations). The X-ray production efficiency is varied over four orders of magnitude and the hardness of the X-ray spectrum is varied from that predicted for high-mass X-ray binaries, to the softer spectrum expected from the hot inter-stellar medium. We find peaks in the redshift evolution of both the variance and skewness associated with the efficiency of X-ray production. The amplitude of the variance is also sensitive to the hardness of the X-ray SED. We find that the relative timing of the coupling and heating phases can be inferred from the redshift extent of a plateau that connects a peak in the variance's evolution associated with Lyman-alpha coupling to the heating peak. Importantly, we find that late X-ray heating would seriously hamper our ability to constrain reionization with the variance. Late X-ray heating also qualitatively alters the evolution of the skewness, providing a clean way to constrain such models. If foregrounds can be removed, we find that LOFAR, MWA and PAPER could constrain reionization and late X-ray heating models with the variance. We find that HERA and SKA (phase 1) will be able to constrain both reionization and heating by measuring the variance using foreground-avoidance techniques. If foregrounds can be removed they will also be able to constrain the nature of Lyman-alpha coupling.Comment: 16 pages, 13 figure, 1 table. Accepted for publication in MNRA

Quantifying the non-Gaussianity in the EoR 21-cm signal through bispectrum

The epoch of reionization (EoR) 21-cm signal is expected to be highly non-Gaussian in nature and this non-Gaussianity is also expected to evolve with the progressing state of reionization. Therefore the signal will be correlated between different Fourier modes ($k$). The power spectrum will not be able capture this correlation in the signal. We use a higher-order estimator -- the bispectrum -- to quantify this evolving non-Gaussianity. We study the bispectrum using an ensemble of simulated 21-cm signal and with a large variety of $k$ triangles. We observe two competing sources driving the non-Gaussianity in the signal: fluctuations in the neutral fraction ($x_{\rm HI}$) field and fluctuations in the matter density field. We find that the non-Gaussian contribution from these two sources vary, depending on the stage of reionization and on which $k$ modes are being studied. We show that the sign of the bispectrum works as a unique marker to identify which among these two components is driving the non-Gaussianity. We propose that the sign change in the bispectrum, when plotted as a function of triangle configuration $\cos{\theta}$ and at a certain stage of the EoR can be used as a confirmative test for the detection of the 21-cm signal. We also propose a new consolidated way to visualize the signal evolution (with evolving $\overline{x}_{\rm HI}$ or redshift), through the trajectories of the signal in a power spectrum and equilateral bispectrum i.e. $P(k)-B(k, k, k)$ space.Comment: 18 pages, 11 figures. Accepted for publication in MNRAS. Replaced to match the accepted versio

Epoch of reionization parameter estimation with the 21-cm bispectrum

We present the first application of the isosceles bispectrum to MCMC parameter inference from the cosmic 21-cm signal. We extend the MCMC sampler 21cmMC to use the fast bispectrum code, BiFFT, when computing the likelihood. We create mock 1000h observations with SKA1-low, using PyObs21 to account for uv-sampling and thermal noise. Assuming the spin temperature is much higher than that of the CMB, we consider two different reionization histories for our mock observations: fiducial and late-reionization. For both models we find that bias on the inferred parameter means and 1-$\sigma$ credible intervals can be substantially reduced by using the isosceles bispectrum (calculated for a wide range of scales and triangle shapes) together with the power spectrum (as opposed to just using one of the statistics). We find that making the simplifying assumption of a Gaussian likelihood with a diagonal covariance matrix does not notably bias parameter constraints for the three-parameter reionization model and basic instrumental effects considered here. This is true even if we use extreme (unlikely) initial conditions which would be expected to amplify biases. We also find that using the cosmic variance error calculated with Monte-Carlo simulations using the fiducial model parameters whilst assuming the late-reionization model for the simulated data also does not strongly bias the inference. This implies we may be able to sparsely sample and interpolate the cosmic variance error over the parameter space, substantially reducing computational costs. All codes used in this work are publicly-available.Comment: 12 pages, 11 figures (submitted to MNRAS

The H I intensity mapping bispectrum including observational effects

The bispectrum is a three-point statistic with the potential to provide additional information beyond power spectra analyses of survey data sets. Radio telescopes that broadly survey the 21-cm emission from neutral hydrogen (H I) are a promising way to probe LSS and in this work we present an investigation into the H I intensity mapping (IM) bispectrum using simulations. We present a model of the redshift space H I IM bispectrum including observational effects from the radio telescope beam and 21-cm foreground contamination. We validate our modelling prescriptions with measurements from robust IM simulations, inclusive of these observational effects. Our foreground simulations include polarization leakage, on which we use a principal component analysis cleaning method. We also investigate the effects from a non-Gaussian beam including side-lobes. For a MeerKAT-like single-dish IM survey at z = 0.39, we find that foreground removal causes an 8 per cent reduction in the equilateral bispectrum’s signal-to-noise ratio, whereas the beam reduces it by 62 per cent. We find our models perform well, generally providing χ2 dof ∼ 1, indicating a good fit to the data. Whilst our focus is on post-reionization, single-dish IM, our modelling of observational effects, especially foreground removal, can also be relevant to interferometers and reionization studies

21cmFAST v3: A Python-integrated C code for generating 3D realizations of the cosmic 21cm signal

This brief code paper presents a new Python-wrapped version of the popular 21cm cosmology simulator, 21cmFAST. The new version, v3+, maintains the same core functionality of previous versions of 21cmFAST, but features a simple and intuitive interface, and a great deal more flexibility. This evolution represents the work of a formalized collaboration, and the new version, available publicly on GitHub, provides a single point-of-reference for all future upgrades and community-added features. In this paper, we describe simple usage of 21cmFAST, some of its new features, and provide a simple performance benchmark

Gaussian process regression for foreground removal in hi intensity mapping experiments

We apply for the first time Gaussian Process Regression (GPR) as a foreground removal technique in the context of single-dish, low redshift H I intensity mapping, and present an open-source PYTHON toolkit for doing so. We use MeerKAT and SKA1-MID-like simulations of 21 cm foregrounds (including polarization leakage), H I cosmological signal, and instrumental noise. We find that it is possible to use GPR as a foreground removal technique in this context, and that it is better suited in some cases to recover the H I power spectrum than principal component analysis (PCA), especially on small scales

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Optimizing future dark energy surveys for model selection goals

We demonstrate a methodology for optimizing the ability of future dark energy surveys to answer model selection questions, such as `Is acceleration due to a cosmological constant or a dynamical dark energy model?'. Model selection Figures of Merit are defined, exploiting the Bayes factor, and surveys optimized over their design parameter space via a Monte Carlo method. As a specific example we apply our methods to generic multi-fibre baryon acoustic oscillation spectroscopic surveys, comparable to that proposed for SuMIRe PFS, and present implementations based on the Savage-Dickey Density Ratio that are both accurate and practical for use in optimization. It is shown that whilst the optimal surveys using model selection agree with those found using the Dark Energy Task Force (DETF) Figure of Merit, they provide better informed flexibility of survey configuration and an absolute scale for performance; for example, we find survey configurations with close to optimal model selection performance despite their corresponding DETF Figure of Merit being at only 50% of its maximum. This Bayes factor approach allows us to interpret the survey configurations that will be good enough for the task at hand, vital especially when wanting to add extra science goals and in dealing with time restrictions or multiple probes within the same project.Comment: 12 pages, 16 figure