arXiv subjects

Ian Hothi

Publications and source records attributed to Ian Hothi.

9 recordsLinked to original sources

arXiv2026

Inferring Cosmology and Astrophysics from the High-redshift 21cm Signal with SKA-Low

The Square Kilometre Array's low frequency telescope (SKA-Low) will enable inference of astrophysical and cosmological parameters from the redshifted 21 cm signal, probing the Cosmic Dawn and Epoch of Reionisation. While the power spectrum is the primary target for initial detection, the inherently non-Gaussian nature of the 21 cm signal, driven by the patchy evolution of ionised regions and spin temperature fluctuations, encodes rich information accessible through higher-order statistics and morphological measurements. Extracting these constraints requires diverse inference tools, encompassing both sophisticated modelling frameworks (analytical, semi-numerical, numerical, and emulators) used to predict the 21 cm signal, and advanced inference techniques (Bayesian, simulation-based, field-level) to connect statistics to the underlying physics. This chapter reviews these tools and explores the constraining power of different statistical probes accessible with SKA-Low, including the power spectrum, statistics beyond order two, moments of the signal distribution, and morphological measures. Combining these complementary statistics is crucial for breaking parameter degeneracies and unveiling the properties of the early Universe. We specifically assess the potential of the initial SKA-Low configuration (AA*) to measure galaxy and IGM properties, demonstrating its capability for early science results. This chapter forms part of a comprehensive set detailing the Epoch of Reionisation and Cosmic Dawn science case for the SKA-Low telescope.

astro-ph.CO↗

arXiv2026

Overview of 21cm Experiments at high redshift with SKAO

We provide an overview of the eight SKAO Science Book chapters that motivate the Epoch of Reionisation and Cosmic Dawn experiments with SKA-Low. We describe the individual SKA-Low experiments and expected sensitivity - power spectrum, tomography, 21-cm forest, cross-correlations, building on the broad observational plan laid out in the 2015 SKA Science Book. Finally, we outline features of the telescope that will be critical for the success of EoR/CD science, e.g., beam apodization, substations, and multi-beaming.

astro-ph.CO↗

arXiv2026

Imaging the 21-cm Signal from the Cosmic Dawn & Epoch of Reionization and the Connection with the Global Signal

The original baseline design for SKA-Low was motivated by the ability to produce tomographic images of the redshifted 21-cm signal, thus allowing the research field to move beyond the simple statistic of the power spectrum. In this chapter we review the imaging capabilities of SKA-Low, the wide variety of methods proposed for quantatively analysing image data, as well as the connection with the global 21-cm signal.

astro-ph.CO↗

arXiv2025

Generative Models of 21cm EoR Lightcones with 3D Scattering Transforms

The 21cm signal from the Epoch of Reionization (EoR) is observed as a three-dimensional data set known as a lightcone, consisting of a redshift (frequency) axis and two spatial sky plane axes. When observed by radio interferometers, this EoR signal is strongly obscured by foregrounds that are several orders of magnitude stronger. Due to its inherently non-Gaussian nature, the EoR signal requires robust statistical tools to accurately separate it from these foreground contaminants, but current foreground separation techniques focus primarily on recovering the EoR power spectrum, often neglecting valuable non-Gaussian information. Recent developments in astrophysics, particularly in the context of the Galactic interstellar medium, have demonstrated the efficacy of scattering transforms - novel summary statistics for highly non-Gaussian processes - for component separation tasks. Motivated by these advances, we extend the scattering transform formalism from two-dimensional data sets to three-dimensional EoR lightcones. To this end, we introduce a 3D wavelet set from the tensor product of 2D isotropic wavelets in the sky plane domain and 1D wavelets in the redshift domain. As generative models form the basis of component separation, our focus here is on building and validating generative models that can be used for component separation in future projects. To achieve this, we construct maximum entropy generative models to synthesise EoR lightcones, and statistically validate the generative model by quantitatively comparing the synthesised EoR lightcones with the single target lightcone used to construct them, using independent statistics such as the power spectrum and Minkowski Functionals. The synthesised lightcones agree well with the target lightcone both statistically and visually, opening up the possibility of developing for component separation methods using 3D scattering transforms.

astro-ph.CO↗

arXiv2024

Exploring the effect of different cosmologies on the Epoch of Reionization 21-cm signal with POLAR

A detection of the 21-cm signal power spectrum from the Epoch of Reionization is imminent, thanks to consistent advancements from telescopes such as LOFAR, MWA, and HERA, along with the development of SKA. In light of this progress, it is crucial to expand the parameter space of simulations used to infer astrophysical properties from this signal. In this work, we explore the role of cosmological parameters such as the Hubble constant $H_0$ and the matter clustering amplitude $\sigma_8$, whose values as provided by measurements at different redshifts are in tension. We run $N$-body simulations using GADGET-4, and post-process them with the reionization simulation code POLAR, that uses L-GALAXIES to include galaxy formation and evolution properties and GRIZZLY to execute 1-D radiative transfer of ionizing photons in the intergalactic medium (IGM). We compare our results with the latest JWST observations and explore which astrophysical properties for different cosmologies are necessary to match the observed UV luminosity functions at redshifts $z = 10$ and $9$. Additionally, we explore the impact of these parameters on the observed 21-cm signal power spectrum, focusing on the redshifts within the range of LOFAR 21-cm signal observations ($z \approx 8.5-10$). Despite differences in cosmological and astrophysical parameters, our models cannot be ruled out by the current upper limits. This suggests the need for broader physical parameter spaces for inference modeling to account for all models that agree with observations. However, we also propose stronger constraining power by using a combination of galactic and IGM observables.

astro-ph.CO↗

arXiv2023

21-cm Signal from the Epoch of Reionization: A Machine Learning upgrade to Foreground Removal with Gaussian Process Regression

In recent years, a Gaussian Process Regression (GPR) based framework has been developed for foreground mitigation from data collected by the LOw-Frequency ARray (LOFAR), to measure the 21-cm signal power spectrum from the Epoch of Reionization (EoR) and Cosmic Dawn. However, it has been noted that through this method there can be a significant amount of signal loss if the EoR signal covariance is misestimated. To obtain better covariance models, we propose to use a kernel trained on the {\tt GRIZZLY} simulations using a Variational Auto-Encoder (VAE) based algorithm. In this work, we explore the abilities of this Machine Learning based kernel (VAE kernel) used with GPR, by testing it on mock signals from a variety of simulations, exploring noise levels corresponding to $\approx$10 nights ($\approx$141 hours) and $\approx$100 nights ($\approx$1410 hours) of observations with LOFAR. Our work suggests the possibility of successful extraction of the 21-cm signal within 2$\sigma$ uncertainty in most cases using the VAE kernel, with better recovery of both shape and power than with previously used covariance models. We also explore the role of the excess noise component identified in past applications of GPR and additionally analyse the possibility of redshift dependence on the performance of the VAE kernel. The latter allows us to prepare for future LOFAR observations at a range of redshifts, as well as compare with results from other telescopes.

astro-ph.CO↗

arXiv2023

Wavelet Based Statistics for Enhanced 21cm EoR Parameter Constraints

We propose a new approach to improve the precision of astrophysical parameter constraints for the 21cm signal from the epoch of reionisation (EoR). Our method introduces new sets of summary statistics, hereafter `evolution compressed' statistics, which quantify the spectral evolution of the 2D spatial statistics computed at fixed redshift. We defined such compressed statistics for power spectrum (PS), wavelet scattering transforms (WST), and wavelet moments (WM), which also characterise non-Gaussian features. To compare these different statistics, along with the 3D power spectrum, we estimated their Fisher information on three cosmological parameters from an ensemble of simulations of 21cm EoR data, both in noiseless and noisy scenarios using Square Kilometre Array (SKA) noise levels equivalent to 100 and 1000 hours of observations. We also compare wavelet statistics, in particular WST, built from standard directional Morlet wavelets, as well as from a set of isotropic wavelets derived from the binning window function of the 2D power spectrum. For the noiseless case, the compressed wavelet statistics give constraints that are up to five times more precise than those obtained from the 3D isotropic power spectrum. At the same time, for 100h SKA noise, from which it is difficult to extract non-Gaussian features, compressed wavelet statistics still give over 30\% tighter constraints. We find that the wavelet statistics with wavelets derived from the power-spectrum binning window function provide the tightest constraints of all the statistics, with the WSTs seemingly performing better than the WMs, in particular when working with noisy data. The findings of this study demonstrate that evolution-compressed statistics extract more information than usual 3D isotropic power-spectra approaches and that our wavelet-based statistics can consistently outmatch power-spectrum-based statistics.

astro-ph.CO↗

arXiv2020

Comparing Foreground Removal Techniques for Recovery of the LOFAR-EoR 21cm Power Spectrum

We compare various foreground removal techniques that are being utilised to remove bright foregrounds in various experiments aiming to detect the redshifted 21cm signal of neutral hydrogen from the Epoch of Reionization. In this work, we test the performance of removal techniques (FastICA, GMCA, and GPR) on 10 nights of LOFAR data and investigate the possibility of recovering the latest upper limit on the 21cm signal. Interestingly, we find that GMCA and FastICA reproduce the most recent 2$\sigma$ upper limit of $\Delta^2_{21} <$ (73)$^2$ mK$^2$ at $k=0.075~ h \mathrm{cMpc}^{-1}$, which resulted from the application of GPR. We also find that FastICA and GMCA begin to deviate from the noise-limit at \textit{k}-scales larger than $\sim 0.1 ~h \mathrm{cMpc}^{-1}$. We then replicate the data via simulations to see the source of FastICA and GMCA's limitations, by testing them against various instrumental effects. We find that no single instrumental effect, such as primary beam effects or mode-mixing, can explain the poorer recovery by FastICA and GMCA at larger \textit{k}-scales. We then test scale-independence of FastICA and GMCA, and find that lower \textit{k}-scales can be modelled by a smaller number of independent components. For larger scales ($k \gtrsim 0.1~h \mathrm{cMpc}^{-1}$), more independent components are needed to fit the foregrounds. We conclude that, the current usage of GPR by the LOFAR collaboration is the appropriate removal technique. It is both robust and less prone to overfitting, with future improvements to GPR's fitting optimisation to yield deeper limits.

astro-ph.CO↗

arXiv2020

The bispectrum and 21cm foregrounds during the Epoch of Reionization

Numerous studies have established the theoretical potential of the 21cm bispectrum to boost our understanding of the Epoch of Reionization (EoR), and therefore early generation of stars and galaxies. In this paper we look at the impact of foregrounds and instrumental effects on the 21cm bispectrum and our ability to measure it. Unlike the power spectrum for which (in the absence of instrumental effects) there is a window clear of smooth-spectrum foregrounds in which the 21cm power spectrum may be detectable, there is no such "EoR window" for the bispectrum. Instead, on smaller scales EoR structure modulates that of the foregrounds (FG) to alter the EoR+FG bispectrum from that of the foregrounds in a complex manner. On larger scales the EoR structures are completely swamped by those of the foregrounds, and the EoR+FG bispectrum is entirely dominated by that of the foregrounds. It is therefore unlikely that the bispectrum will be useful in cases where we are observationally restricted to using foreground avoidance techniques. We also find that there is potential for instrumental effects to seriously corrupt the bispectrum, possibly even rendering the bispectrum useless for parameter estimation. On larger scales, foreground removal using GMCA is found to recover the EoR bispectrum to a reasonable level of accuracy (even better than the power spectrum for certain configurations). Further studies are necessary to understand the error and/or bias associated with foreground removal before the 21cm bispectrum can be practically applied in analysis of future data.

astro-ph.CO↗