arXiv subjects

Hayato Shimabukuro

Publications and source records attributed to Hayato Shimabukuro.

At least 19 recordsLinked to original sources

arXiv2026

Machine Learning and the SKA for Cosmic Dawn and the Epoch of Reionization

When operational, the SKA will generate unprecedented amounts of data and provide exquisite sensitivity for 21 cm tomography of Cosmic Dawn (CD) and the Epoch of Reionization (EoR). With this comes opportunities for new data-driven algorithms that unlock new methods for instrument modelling, data analysis, theoretical simulation, and inference for understanding the high-redshift universe. In this chapter, we provide an overview of some machine learning algorithms that have been proposed for CD and EoR science with the SKA

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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.

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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.

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arXiv2026

Exploring the Cosmic Dawn through the 21 cm Forest and High-redshift Radio Sources with the SKA

The 21~cm forest, seen as absorption features in the spectra of distant radio sources, is produced by intervening neutral hydrogen and offers a direct probe of the neutral intergalactic medium during the epoch of reionization (EoR). Because it is sensitive to small-scale structure and gas temperature, it can constrain the thermal history of the early Universe and physics that affects structure formation. Detecting individual absorption lines is challenging, mainly because of their weakness and the scarcity of high-redshift radio-bright sources. Recent progress, however, has made 21~cm forest studies increasingly feasible: new statistical observables can improve sensitivity within realistic observing times, updated radio-source counts have revised expectations for suitable background quasars, and deep-learning methods can extract physical information more efficiently. In addition, new approaches have been developed to separate astrophysical effects from early galaxies from fundamental-physics effects on small-scale structure. With the Square Kilometre Array (SKA), the 21~cm forest will therefore provide a promising route to study early heating, possible exotic energy injection, dark matter properties, neutrino mass, the running spectral index, and baryon--dark-matter relative velocity. This chapter reviews recent developments in 21~cm forest research and discusses observational strategies and prospects for constraining the first galaxies and fundamental physics with SKA-Low.

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arXiv2026

Reconstruction of Reionization Histories from 21 cm Power-Spectrum Evolution with Artificial Neural Networks

We investigate whether the redshift evolution of the fixed-$k$ dimensionless 21 cm power spectrum, $\Delta^2_{21}(k, z)$, contains sufficient information to reconstruct reionization histories $x_{\rm HI}(z)$ with artificial neural networks. Using semi-numerical realizations generated within a restricted three-parameter 21cmFAST model family, we train a compact feed-forward network to learn the inverse mapping from power-spectrum trajectories to the neutral-fraction history on a 97-point redshift grid spanning $6 \le z \le 15$. For $k = 0.1$, $0.5$, and $1.0\ h\ \mathrm{Mpc}^{-1}$, representative tests on an independent test set show that the midpoint redshift $z_{50}$ is recovered more accurately than the duration $\Delta z = z_{75} - z_{25}$ in terms of dimensionless relative errors: $z_{50}$ is reconstructed with $\mathrm{MAE} = 0.0046$ and $\mathrm{RMSE} = 0.0100$, whereas $\Delta z$ yields $\mathrm{MAE} = 0.0302$ and $\mathrm{RMSE} = 0.0378$. This result indicates that fixed-$k$ power-spectrum evolution carries stronger information about the timing of reionization than about the detailed width of the transition within the adopted prior. We further test an idealized foreground-free SKA1-Low-like thermal-plus-sample-variance noise model and find that the reconstruction remains stable in the favorable signal-to-noise regime considered here. These results demonstrate that neural networks can serve as prior-dependent inverse mapping for reconstructing reionization histories from 21 cm power-spectrum evolution.

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arXiv2026

Application of Machine Learning to 21 cm Cosmology

This chapter reviews applications of machine learning (ML) to redshifted 21 cm cosmology, focusing on cosmic dawn, the Epoch of Reionization, and SKA-Low science. The redshifted 21 cm line directly probes diffuse neutral hydrogen, but the measured signal is not a simple astrophysical observable: density, ionization, heating, radiation backgrounds, foreground treatment, and instrumental response are coupled. The chapter first summarizes the physical ingredients needed in later sections, including the global signal, spatial fluctuations, morphology-sensitive statistics, and the 21 cm forest. It then discusses the main barriers to interpretation: bright foregrounds, radio-frequency interference, ionospheric and calibration effects, incomplete sampling, and the cost of forward modeling in high-dimensional parameter spaces. ML applications are organized by their role in the analysis chain. Observation-domain methods act on contaminated data products; theory-domain methods accelerate or compress forward modeling; and inference-domain methods connect observables to astrophysical and cosmological parameters.

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arXiv2026

Inferring population III star properties from the 21-cm global signal

Investigating the properties of the first stars in the universe, known as Population III (Pop~III) stars, is essential, yet it remains an open question. One way to explore these stars is by examining their effects on the surrounding gas during the cosmic dawn. In this study, we investigate whether the 21-cm global signal can constrain the typical mass and star formation efficiency of first-generation stars. We perform semi-numerical simulations that include the escape fraction of ionizing photons, which depends on stellar and halo masses, as well as the heating structure surrounding a halo that hosts the first star, determined by radiation hydrodynamics (RHD) simulations. Using a Fisher analysis that includes thermal noise for an integration time of $1000\,\mathrm{h}$, we find that the global signal provides information for constraining these properties if the foreground spectrum can be perfectly removed. However, when the smooth foreground spectrum is modeled using a log-polynomial and inferred from the same data, its spectral variation becomes strongly degenerate with the changes produced by the Pop~III parameters, substantially weakening the constraints. These results demonstrate that accurate foreground modeling and removal are essential for extracting information about Pop~III properties from the global 21-cm signal.

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arXiv2026

Topological Signatures of Heating and Dark Matter in the 21 cm Forest

We show that persistence-based topology of the 21 cm forest encodes information about Cosmic Dawn that is complementary to traditional amplitude- or correlation-based statistics. Applying topological data analysis to simulated one-dimensional forest spectra over a grid of X-ray heating efficiencies $f_X$ and warm-dark-matter masses $m_{\rm WDM}$ (which set the free-streaming scale), we construct persistence diagrams and Betti-0 curves that track the birth-merger hierarchy of absorption troughs under sublevel filtrations. From these summaries we define three interpretable descriptors: the trough line density $λ(t_\star)$, the total squared persistence $M_2=\sum_{j\in I_{\rm long}}τ_j^2$, and the Betti-curve asymmetry $A_{\rm skew}$. In a Fisher forecast around a fiducial WDM model, $λ(t_\star)$ and $A_{\rm skew}$ provide strong local leverage on the heating axis, while $M_2$ retains appreciable sensitivity to the free-streaming scale and supplies an inclined constraint direction that reduces the remaining degeneracy in the $(f_X,m_{\rm WDM})$ plane. We further demonstrate that, under an SKA1-Low-like uncorrelated thermal-noise model, noise predominantly produces short-lived fluctuations that are removed by a uniform persistence cut, leaving the topology of long-lived troughs and the gross Betti-curve morphology largely intact. These results establish persistence-based descriptors as a robust non-Gaussian probe of small-scale structure and heating during Cosmic Dawn, naturally complementing power-spectrum and wavelet-based analyses.

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arXiv2025

Wavelet-Scattering Signatures of Fuzzy Dark Matter in Simulated 21 cm Brightness-Temperature Maps

We study the effect of fuzzy dark matter (FDM) on the multiscale morphology of simulated redshifted 21\,cm maps during Cosmic Dawn and the Epoch of Reionization. Using FDM-modified \texttt{21cmFAST} light cones, we apply the two-dimensional wavelet scattering transform (WST) to matched 2\,MHz map products. The first-order coefficients $S_1(j)$ summarize wavelet-band amplitudes, while the normalized second-order ratio $R=S_2/S_1(j_1)$ measures ordered cross-scale modulation. FDM shifts and reshapes both summaries through delayed halo and source formation. We compare a two-dimensional power spectrum (PS), WST, and their combination on the same transferred and noisy maps. In this controlled local Fisher analysis, PS+WST reduces the marginalized errors on the FDM mass, effective X-ray emissivity normalization, and ionizing efficiency by approximately a factor of 1.8 relative to the matched two-dimensional PS baseline, although the dominant mass-heating degeneracy remains. An idealized three-wedge test shows that $R$ is less reshaped at the coefficient level than $S_1$. The covariance includes thermal noise conditional on one fiducial light cone but not cosmic variance or foreground residuals; the results are therefore relative information comparisons, not survey forecasts or a demonstration of superiority over a full three-dimensional PS analysis.

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arXiv2025

Probing initial isocurvature perturbation with 21cm one-point statistics

Isocurvature perturbation--expected from multi-field inflation models--can leave unique signatures in the early Universe, but remain weakly constrained, especially on small scales. In this work, we investigate the constraining power of one-point statistics (variance and skewness) of the 21cm brightness temperature during Cosmic Dawn and the Epoch of Reionization, using semi-numerical simulations from 21cmFAST. We model both adiabatic and cold dark matter isocurvature modes, exploring their impact on the matter power spectrum, the timing of structure formation, and the evolution of neutral hydrogen. By varying astrophysical parameters as well as the isocurvature fraction and spectral index, we quantify their respective effects on the 21cm power spectrum and on one-point statistics, using the former as a physical diagnostic and the latter as the basis of our final forecast. Our results show that while variance is highly sensitive to the timing of cosmic events and provides tight constraints on isocurvature parameters, skewness is more strongly affected by astrophysical uncertainties and observational noise. Incorporating realistic instrumental noise based on SKA configurations, we perform a Fisher analysis using only the redshift evolution of the one-point statistics, and demonstrate that the isocurvature fraction can be constrained down to the percent level, though a strong degeneracy with the spectral index remains. We discuss the importance of complementary probes, such as the 21cm forest and galaxy surveys, to break these parameter degeneracies. Our findings highlight the power of 21cm one-point statistics as robust and independent tools for probing early-Universe physics beyond what is accessible with traditional power spectrum analyses.

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arXiv2025

Analyzing the 21cm forest with Wavelet Scattering Transform: Insight into non-Gaussian features of the 21cm forest

The 21cm forest, narrow absorption features in the spectra of high redshift radio sources caused by intervening neutral hydrogen, offers a unique probe of the intergalactic medium and small-scale structures during reionization. While traditional power spectrum methods have been widely used for analyzing the 21cm forest, these techniques are limited in capturing the non-Gaussian nature of the signal. In this work, we introduce the Wavelet Scattering Transform (WST) as a novel diagnostic tool for the 21cm forest, which allows for the extraction of higher-order statistical features that power spectrum methods cannot easily capture. By decomposing simulated brightness temperature spectra into a hierarchy of scattering coefficients, the WST isolates both local intensity fluctuations (first-order coefficients) and scale-scale correlations (second-order coefficients), revealing the complex, multi-scale non-Gaussian interactions inherent in the 21cm forest. This approach enhances the power of 21cm forest in distinguishing between different cosmological models, such as Cold Dark Matter (CDM) and Warm Dark Matter (WDM), as well as scenarios with enhanced X-ray heating. Unlike traditional methods, which focus primarily on Gaussian statistics, the WST captures richer astrophysical and cosmological information. Our analysis shows that WST can significantly improve constraints on key parameters, such as the X-ray heating efficiency and the WDM particle mass, providing deeper insights into the early stages of cosmic structure formation.

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arXiv2025

Nonlinear reconstruction of 21cm global signal from 21cm power spectrum with artificial neural networks

In this paper, we propose a novel method to recover the 21cm global signal from the 21cm power spectrum using artificial neural networks (ANNs). The 21cm global signal is crucial for understanding cosmic evolution from the Dark Ages through the Epoch of Reionization (EoR). While interferometers like LOFAR, MWA, HERA, and SKA focus on detecting the 21cm power spectrum, single-dish experiments such as EDGES target the global signal. Our method utilizes ANNs to establish a connection between these two observables, providing a means to cross-validate independent 21cm line observations. This capability is significant as it allows different observational approaches to verify each other's results, ensuring greater reliability in 21cm cosmology. We demonstrate that our ANN-based approach can accurately recover the 21cm global signal across a wide redshift range (z=7.5-35) from simulated data, even when realistic thermal noise levels, such as those expected from SKA-1, are considered. This cross-validation process strengthens the robustness of 21cm signal analysis, offering a more comprehensive understanding of the early universe.

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arXiv2023

Impact of dark matter-baryon relative velocity on the 21cm forest

We study the effect of the relative velocity between the dark matter (DM) and the baryon on the 21cm forest signals. The DM-baryon relative velocity arises due to their different evolutions before the baryon-photon decoupling epoch and it gives an additional anisotropic pressure that can suppress the perturbation growth. It is intriguing that the scales $k\sim {\cal O}(10\sim 10^3)h/\mathrm{Mpc}$ at which the matter power spectrum is affected by such a streaming velocity turns out to be the scale at which the 21cm forest signal is sensitive to. We demonstrate that the 21cm absorption line abundance can decrease by more than a factor of a few due to the small-scale matter power spectrum suppression caused by the DM-baryon relative velocity.

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arXiv2023

Exploring the cosmic dawn and epoch of reionization with 21cm line

The dark age of the universe, when no luminous object had existed, ended with the birth of the first stars, galaxies, and blackholes. This epoch is called cosmic dawn. Cosmic reionization is the major transition of the intergalactic medium (IGM) in the universe driven by ionizing photons emitted from luminous objects. Although the epoch through the dark age to reionization is a milestone in the universe, our knowledge of this epoch has not been sufficient yet. Cosmic 21cm signal, which is emitted from neutral hydrogen, is expected to open a new window for this epoch. In this review paper, we first introduce the basic physics of the 21cm line and how first stars impact on the 21cm line signal. Next, we briefly summarize how we extract astrophysical information from the 21cm line signal by means of statistical and machine learning approaches. We also discuss the synergy between the 21cm line signal and other emission lines. Finally, we summarize the current status of 21cm experiments.

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arXiv2022

Estimation of HII Bubble Size Distribution from 21cm Power Spectrum with Artificial Neural Networks

The bubble size distribution of ionized hydrogen regions probes the information about the morphology of \HII\ bubbles during the reionization. Conventionally, the \HII\ bubble size distribution can be derived from the tomographic imaging data of the redshifted 21~cm signal from the epoch of reionization, which, however, is observationally challenging even for the upcoming large radio interferometer arrays. Given that these interferometers promise to measure the 21~cm power spectrum accurately, we propose a new method, which is based on the artificial neural networks (ANN), to reconstruct the \HII\ bubble size distribution from the 21~cm power spectrum. We demonstrate that the reconstruction from the 21~cm power spectrum can be almost as accurate as directly measured from the imaging data with the fractional error $\lesssim 10\%$, even with thermal noise at the sensitivity level of the Square Kilometre Array. Nevertheless, the reconstruction implicitly exploits the modelling in reionization simulations, and hence the recovered \HII\ bubble size distribution is not an independent summary statistic from the power spectrum, and should be used only as the indicator for understanding \HII\ bubble morphology and its evolution.

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arXiv2021

Predicting 21cm-line map from Lyman $α$ emitter distribution with Generative Adversarial Networks

The radio observation of 21\,cm-line signal from the Epoch of Reionization (EoR) enables us to explore the evolution of galaxies and intergalactic medium in the early universe. However, the detection and imaging of the 21\,cm-line signal are tough due to the foreground and instrumental systematics. In order to overcome these obstacles, as a new approach, we propose to take a cross correlation between observed 21\,cm-line data and 21\,cm-line images generated from the distribution of the Lyman-$α$ emitters (LAEs) through machine learning. In order to create 21\,cm-line maps from LAE distribution, we apply conditional Generative Adversarial Network (cGAN) trained with the results of our numerical simulations. We find that the 21\,cm-line brightness temperature maps and the neutral fraction maps can be reproduced with correlation function of 0.5 at large scales $k<0.1~{\rm Mpc}^{-1}$. Furthermore, we study the detectability of the the cross correlation assuming the the LAE deep survey of the Subaru Hyper Suprime Cam, the 21\,cm observation of the MWA Phase II and the presence of the foreground residuals. We show that the signal is detectable at $k < 0.1~{\rm Mpc}^{-1}$ with 1000 hours of MWA observation even if the foreground residuals are 5 times larger than the 21\,cm-line power spectrum. Our new approach of cross correlation with image construction using the cGAN can not only boost the detectability of EoR 21\,cm-line signal but also allow us to estimate the 21\,cm-line auto-power spectrum.

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arXiv2020

21cm forest probes on the axion dark matter in the post-inflationary Peccei-Quinn symmetry breaking scenarios

We study the future prospects of the 21cm forest observations on the axion-like dark matter when the spontaneous breaking of the global Peccei-Quinn (PQ) symmetry occurs after the inflation. The large isocurvature perturbations of order unity sourced from axion-like particles can result in the enhancement of minihalo formation, and the subsequent hierarchical structure formation can affect the minihalo abundance whose masses can exceed ${\cal O}(10^4) M_{\odot}$ relevant for the 21cm forest observations. We show that the 21cm forest observations are capable of probing the axion-like particle mass in the range $10^{-18}\lesssim m_a \lesssim 10^{-12}$ eV for the temperature independent axion mass. For the temperature dependent axion mass, the zero temperature axion mass scale for which the 21cm forest measurements can be affected is extended further to as big as of order $10^{-6}$ eV.

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arXiv2020

Constraints on nature of ultra light dark matter particles with 21cm forest

The ultra-light scalar fields can arise ubiquitously, for instance, as a result of the spontaneous breaking of an approximate symmetry such as the axion and more generally the axion-like particles. In addition to the particle physics motivations, these particles can also play a major role in cosmology by contributing to dark matter abundance and affecting the structure formation at sub-Mpc scales. In this paper, we propose to use the 21cm forest observations to probe the nature of ultra-light dark matter. The 21cm forest can probe much smaller scales than the Lyman-$α$ forest, that is, $k\gtrsim 10\mathrm{Mpc}^{-1}$. We explore the range of the ultra-light dark matter mass $m_{u}$ and $f_u$, the fraction of ultra-light dark matter with respect to the total matter, which can be probed by the 21cm forest. We find that 21cm forest can potentially put the dark matter mass lower bound $m_u \gtrsim 10^{-18}$ eV for $f_u=1$, which is 3 orders of magnitude bigger mass scale than those probed by the current Lyman-$α$ forest observations.While the effects of the ultra-light particles on the structure formation become smaller when the dominant component of dark matter is composed of the conventional cold dark matter, we find that the 21cm forest is still powerful enough to probe the sub-component ultra-light dark matter mass up to the order of $10^{-19}$ eV. The Fisher matrix analysis shows that $(m_u,f_u)\sim (10^{-20}\mathrm{eV}, 0.3)$ is the most optimal parameter set which the 21cm forest can probe with the minimal errors for a sub-component ultra-light dark matter scenario.

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