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We predict the 21-cm global signal and power spectra during the Epoch of Reionisation using the MERAXES semi-analytic galaxy formation and reionisation model, updated to include X-ray heating and thermal evolution of the intergalactic medium. Studying the formation and evolution of galaxies together with the reionisation of cosmic hydrogen using semi-analytic models (such as MERAXES) requires N-body simulations within large volumes and high mass resolutions. For this, we use a simulation of side-length $210~h^{-1}$ Mpc with $4320^3$ particles resolving dark matter haloes to masses of $5\times10^8~h^{-1}~M_\odot$. To reach the mass resolution of atomically cooled galaxies, thought to be the dominant population contributing to reionisation, at $z=20$ of $\sim 2\times10^7~h^{-1}~M_\odot$, we augment this simulation using the DARKFOREST Monte-Carlo merger tree algorithm (achieving an effective particle count of $\sim10^{12}$). Using this augmented simulation we explore the impact of mass resolution on the predicted reionisation history as well as the impact of X-ray heating on the 21-cm global signal and the 21-cm power spectra. We also explore the cosmic variance of 21-cm statistics within $70^{3}$ $h^{-3}$ Mpc$^3$ sub-volumes. We find that the midpoint of reionisation varies by $\Delta z\sim0.8$ and that the cosmic variance on the power spectrum is underestimated by a factor of $2-4$ at $k\sim 0.1-0.4$ Mpc$^{-1}$ due to the non-Gaussian nature of the 21-cm signal. To our knowledge, this work represents the first model of both reionisation and galaxy formation which resolves low-mass atomically cooled galaxies while simultaneously sampling sufficiently large scales necessary for exploring the effects of X-rays in the early Universe., Comment: 17 pages, 13 figures, revised and MNRAS accepted version

Detecting the 21-cm hyperfine transition from neutral hydrogen in the intergalactic medium is our best probe for understanding the astrophysical processes driving the Epoch of Reionisation (EoR). The primary means for a detection of this 21-cm signal is through a statistical measurement of the spatial fluctuations using the 21-cm power spectrum (PS). However, the 21-cm signal is non-Gaussian meaning the PS, which only measures the Gaussian fluctuations, is sub-optimal for characterising all of the available information. The upcoming Square Kilometre Array (SKA) will perform a deep, 1000 hr observation over 100 deg$.^{2}$ specifically designed to recover direct images of the 21-cm signal. In this work, we use the Wavelet Scattering Transform (WST) to extract the non-Gaussian information directly from these two-dimensional images of the 21-cm signal. The key advantage of the WST is its stability with respect to statistical noise for measuring non-Gaussian information, unlike the bispectrum whose statistical noise diverges. We introduce a novel method to isolate this non-Gaussian information from mock 21-cm images and demonstrate its detection at 150 (177)~MHz ($z\sim8.5$ and $\sim7$) for a fiducial model with signal-to-noise of $\sim$5~(8) assuming perfect foreground removal and $\sim2$~(3) assuming foreground wedge avoidance., 17 pages, 10 figures and 1 table. Submitted to MNRAS, comments welcome

Precision calibration poses challenges to experiments probing the redshifted 21-cm signal of neutral hydrogen from the Cosmic Dawn and Epoch of Reionization (z~30-6). In both interferometric and global signal experiments, systematic calibration is the leading source of error. Though many aspects of calibration have been studied, the overlap between the two types of instruments has received less attention. We investigate the sky based calibration of total power measurements with a HERA dish and an EDGES style antenna to understand the role of auto-correlations in the calibration of an interferometer and the role of sky in calibrating a total power instrument. Using simulations we study various scenarios such as time variable gain, incomplete sky calibration model, and primary beam model. We find that temporal gain drifts, sky model incompleteness, and beam inaccuracies cause biases in the receiver gain amplitude and the receiver temperature estimates. In some cases, these biases mix spectral structure between beam and sky resulting in spectrally variable gain errors. Applying the calibration method to the HERA and EDGES data, we find good agreement with calibration via the more standard methods. Although instrumental gains are consistent with beam and sky errors similar in scale to those simulated, the receiver temperatures show significant deviations from expected values. While we show that it is possible to partially mitigate biases due to model inaccuracies by incorporating a time-dependent gain model in calibration, the resulting errors on calibration products are larger and more correlated. Completely addressing these biases will require more accurate sky and primary beam models., Comment: 16 pages, 13 figures, 1 table; accepted for publication in MNRAS main journal

The key challenge in the observation of the redshifted 21-cm signal from cosmic reionization is its separation from the much brighter foreground emission. Such separation relies on the different spectral properties of the two components, although, in real life, the foreground intrinsic spectrum is often corrupted by the instrumental response, inducing systematic effects that can further jeopardize the measurement of the 21-cm signal. In this paper, we use Gaussian Process Regression to model both foreground emission and instrumental systematics in $\sim 2$ hours of data from the Hydrogen Epoch of Reionization Array. We find that a simple co-variance model with three components matches the data well, giving a residual power spectrum with white noise properties. These consist of an "intrinsic" and instrumentally corrupted component with a coherence-scale of 20 MHz and 2.4 MHz respectively (dominating the line of sight power spectrum over scales $k_{\parallel} \le 0.2$ h cMpc$^{-1}$) and a baseline dependent periodic signal with a period of $\sim 1$ MHz (dominating over $k_{\parallel} \sim 0.4 - 0.8$h cMpc$^{-1}$) which should be distinguishable from the 21-cm EoR signal whose typical coherence-scales is $\sim 0.8$ MHz., 15 pages, 15 figures, 1 table, Accepted to MNRAS

The 21-cm power spectrum (PS) has been shown to be a powerful discriminant of reionization and cosmic dawn astrophysical parameters. However, the 21-cm tomographic signal is highly non-Gaussian. Therefore there is additional information which is wasted if only the PS is used for parameter recovery. Here we showcase astrophysical parameter recovery directly from 21-cm images, using deep learning with convolutional neural networks (CNN). Using a data base of 2D images taken from 10 000 21-cm light-cones (each generated from different cosmological initial conditions), we show that a CNN is able to recover parameters describing the first galaxies: (i) T-vir, their minimum host halo virial temperatures (or masses) capable of hosting efficient star formation; (ii) zeta, their typical ionizing efficiencies; (iii) L-X/SFR, their typical soft-band X-ray luminosity to star formation rate; and (iv) E-0, the minimum X-ray energy capable of escaping the galaxy into the IGM. For most of their allowed ranges, log T-vir and log L-X/SFR are recovered with < 1 per cent uncertainty, while zeta and E-0 are recovered with similar to 10 per cent uncertainty. Our results are roughly comparable to the accuracy obtained from Monte Carlo Markov Chain sampling of the PS with 21CMMC for the two mock observations analysed previously, although we caution that we do not yet include noise and foreground contaminants in this proof-of-concept study.

We compute robust lower limits on the spin temperature, $T_{\rm S}$, of the $z=8.4$ intergalactic medium (IGM), implied by the upper limits on the 21-cm power spectrum recently measured by PAPER-64. Unlike previous studies which used a single epoch of reionization (EoR) model, our approach samples a large parameter space of EoR models: the dominant uncertainty when estimating constraints on $T_{\rm S}$. Allowing $T_{\rm S}$ to be a free parameter and marginalizing over EoR parameters in our Markov Chain Monte Carlo code 21CMMC, we infer $T_{\rm S}\ge3 {\rm K}$ (corresponding approximately to $1\sigma$) for a mean IGM neutral fraction of $\bar{x}_{\rm H{\scriptsize I}}\gtrsim0.1$. We further improve on these limits by folding-in additional EoR constraints based on: (i) the dark fraction in QSO spectra, which implies a strict upper limit of $\bar{x}_{\rm H{\scriptsize I}}[z=5.9]\leq 0.06+0.05 \,(1\sigma)$; and (ii) the electron scattering optical depth, $\tau_{\rm e}=0.066\pm0.016\,(1\sigma)$ measured by the Planck satellite. By restricting the allowed EoR models, these additional observations tighten the approximate $1\sigma$ lower limits on the spin temperature to $T_{\rm S} \ge 6$ K. Thus, even such preliminary 21-cm observations begin to rule out extreme scenarios such as `cold reionization', implying at least some prior heating of the IGM. The analysis framework developed here can be applied to upcoming 21-cm observations, thereby providing unique insights into the sources which heated and subsequently reionized the very early Universe., Comment: 7 pages, 1 figure, accepted to MNRAS (matches online version)

We introduce 21CMMC: a parallelized, Monte Carlo Markov Chain analysis tool, incorporating the epoch of reionization (EoR) seminumerical simulation 21CMFAST. 21CMMC estimates astrophysical parameter constraints from 21 cm EoR experiments, accommodating a variety of EoR models, as well as priors on model parameters and the reionization history. To illustrate its utility, we consider two different EoR scenarios, one with a single population of galaxies (with a mass-independent ionizing efficiency) and a second, more general model with two different, feedback-regulated populations (each with mass-dependent ionizing efficiencies). As an example, combining three observations (z=8, 9 and 10) of the 21 cm power spectrum with a conservative noise estimate and uniform model priors, we find that interferometers with specifications like the Low Frequency Array/Hydrogen Epoch of Reionization Array (HERA)/Square Kilometre Array 1 (SKA1) can constrain common reionization parameters: the ionizing efficiency (or similarly the escape fraction), the mean free path of ionizing photons and the log of the minimum virial temperature of star-forming haloes to within 45.3/22.0/16.7, 33.5/18.4/17.8 and 6.3/3.3/2.4 per cent, ~$1\sigma$ fractional uncertainty, respectively. Instead, if we optimistically assume that we can perfectly characterize the EoR modelling uncertainties, we can improve on these constraints by up to a factor of ~few. Similarly, the fractional uncertainty on the average neutral fraction can be constrained to within $\lesssim10$ per cent for HERA and SKA1. By studying the resulting impact on astrophysical constraints, 21CMMC can be used to optimize (i) interferometer designs; (ii) foreground cleaning algorithms; (iii) observing strategies; (iv) alternative statistics characterizing the 21 cm signal; and (v) synergies with other observational programs., Comment: Accepted to MNRAS, matches online version. 20 pages, 4 tables, 11 colour figures

Large surveys for Lyα emitting (LAE) galaxies have been proposed as a new method for measuring clustering of the galaxy population at high redshift with the goal of determining cosmological parameters. However, Lyα radiative transfer effects may modify the observed clustering of LAE galaxies in a way that mimics gravitational effects, potentially reducing the precision of cosmological constraints. We investigate the impact of Lyα radiative transfer effects on the observed clustering of LAE galaxies. In particular, we focus on the effects of the intergalactic medium velocity gradients, local density within the environment of an LAE galaxy and ionizing background fluctuations. For example, the effect of the linear redshift-space distortion on the power spectrum of LAE galaxies is potentially degenerate with Lyα radiative transfer effects owing to the dependence of observed flux on intergalactic medium velocity gradients. In this paper, we show that the three-point function (bispectrum) can distinguish between gravitational and non-gravitational effects, and thus breaks these degeneracies, making it possible to recover cosmological parameters from LAE galaxy surveys. Constraints on the angular diameter distance and Hubble expansion rate are independent of Lyα radiative transfer degeneracies; however, they incur slight reductions in their constraining power resulting from the overall reduction of the signal-to-noise due to the Lyα radiative transfer effects. Combining the power spectrum and bispectrum measurements provides improved constraints on the angular diameter distance and Hubble expansion rate. © 2013 The Authors. Published by Oxford University Press on behalf of the Royal Astronomical Society.

High redshift measurements of the baryonic acoustic oscillation scale (BAO) from large Ly-alpha forest surveys represent the next frontier of dark energy studies. As part of this effort, efficient simulations of the BAO signature from the Ly-alpha forest will be required. We construct a model for producing fast, large volume simulations of the Ly-alpha forest for this purpose. Utilising a calibrated semi-analytic approach, we are able to run very large simulations in 1 Gpc^3 volumes which fully resolve the Jeans scale in less than a day on a desktop PC using a GPU enabled version of our code. The Ly-alpha forest spectra extracted from our semi-analytical simulations are in excellent agreement with those obtained from a fully hydrodynamical reference simulation. Furthermore, we find our simulated data are in broad agreement with observational measurements of the flux probability distribution and 1D flux power spectrum. We are able to correctly recover the input BAO scale from the 3D Ly-alpha flux power spectrum measured from our simulated data, and estimate that a BOSS-like 10^4 deg^2 survey with ~15 background sources per square degree and a signal-to-noise of ~5 per pixel should achieve a measurement of the BAO scale to within ~1.4 per cent. We also use our simulations to provide simple power-law expressions for estimating the fractional error on the BAO scale on varying the signal-to-noise and the number density of background sources. The speed and flexibility of our approach is well suited for exploring parameter space and the impact of observational and astrophysical systematics on the recovery of the BAO signature from forthcoming large scale spectroscopic surveys.