arXiv subjects

Michele Bianco

Publications and source records attributed to Michele Bianco.

At least 19 recordsLinked to original sources

arXiv2026

Implicit inference of the reionization history with higher-order statistics of the 21-cm signal

The Epoch of Reionization (EoR), when the first luminous sources ionised the intergalactic medium, represents a new frontier in cosmology. The Square Kilometre Array Observatory (SKAO) will offer unprecedented insights into this era through observations of the redshifted 21-cm signal, enabling constraints on the Universe's reionization history. We investigate the information content of the average neutral hydrogen fraction ($\bar{x}_{\rm HI}$) in several Gaussian (spherical and cylindrical power spectra) and non-Gaussian (Betti numbers and bispectrum) summary statistics of the 21-cm signal. Mock 21-cm observations are generated using the AA* configuration of SKAO's low-frequency telescope, incorporating noise levels for 100 and 1000 hours. We employ a state-of-the-art implicit inference framework to learn posterior distributions of $\bar{x}_{\rm HI}$ in redshift bins centred at $z=8.0,7.2$ and $6.5$, for each statistic and noise scenario, validating the posteriors through calibration tests. Using the figure of merit to assess constraining power, we find that Betti numbers alone are on average more informative than the power spectra, while the bispectrum provides limited constraints. However, combining higher-order statistics with the cylindrical power spectrum improves the mean figure of merit by $\sim$0.25 dex ($\sim33\%$ reduction in $σ(\bar{x}_{\rm HI})$). The relative contribution of each statistic varies with the stage of reionization. With SKAO observations approaching, our results show that combining power spectra with higher-order statistics can significantly increase the information retrieved from the EoR, maximising the scientific return of future 21-cm observations.

astro-ph.CO↗

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

astro-ph.IM↗

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

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

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↗

arXiv2026

Singular del Pezzo surfaces and isotropic flag varieties

We compute the Chow quotient of the complete flag variety of isotropic subspaces of a four dimensional complex vector space with respect to a skew/symmetric form, and show that it is a singular del Pezzo surface of degree four.

math.AG↗

arXiv2025

Mapping neutral islands during end stages of reionization with photometric intergalactic medium tomography

During the epoch of reionization (EoR), the first generation of luminous sources in our Universe emitted ionizing photons that almost completely ionized the gas in the intergalactic medium (IGM). The growth of ionized bubbles and the persistence of neutral islands within the IGM hold vital clues to understanding the morphology and timeline of cosmic reionization. We explore the potential of photometric IGM tomography using deep narrow-band (NB) imaging to observe the Lyman-$α$ forest transmission in background galaxies with the Subaru/Hyper-Suprime Cam (HSC). Based on our simulations, we find that the currently available NB filter is suitable for mapping the IGM at $z\simeq 5.7$, corresponding to the late stages of reionization. Our findings indicate that over $\sim$500 background galaxies are needed to accurately reconstruct the IGM at scales greater than 200 Mpc, achieving more than a 40 per cent correlation with the true distribution. This technique can help detect final remaining neutral islands that span more than 20 Mpc lengths. Using the superpixel method built to identify physical patterns in noisy image data, we find that the neutral island size distribution can be recovered with an accuracy of $\sim$0.3 dex. Furthermore, we demonstrate that these reconstructed maps are correlated with the galaxy distribution and anti-correlated with the cosmological 21-cm signal from neutral hydrogen in the IGM. Lastly, we find that these reconstructed maps are anti-correlated with the patchy optical depth to the cosmic microwave background. As such, multiple measurements can be employed for confirmed detection of neutral islands during the end stages.

astro-ph.GA↗

arXiv2025

Exploring the Early Universe with Deep Learning

Hydrogen is the most abundant element in our Universe. The first generation of stars and galaxies produced photons that ionized hydrogen gas, driving a cosmological event known as the Epoch of Reionization (EoR). The upcoming Square Kilometre Array Observatory (SKAO) will map the distribution of neutral hydrogen during this era, aiding in the study of the properties of these first-generation objects. Extracting astrophysical information will be challenging, as SKAO will produce a tremendous amount of data where the hydrogen signal will be contaminated with undesired foreground contamination and instrumental systematics. To address this, we develop the latest deep learning techniques to extract information from the 2D power spectra of the hydrogen signal expected from SKAO. We apply a series of neural network models to these measurements and quantify their ability to predict the history of cosmic hydrogen reionization, which is connected to the increasing number and efficiency of early photon sources. We show that the study of the early Universe benefits from modern deep learning technology. In particular, we demonstrate that dedicated machine learning algorithms can achieve more than a $0.95$ $R^2$ score on average in recovering the reionization history. This enables accurate and precise cosmological and astrophysical inference of structure formation in the early Universe.

astro-ph.CO↗

arXiv2025

MuCol Milestone Report No. 7: Consolidated Parameters

This document is comprised of a collection of consolidated parameters for the key parts of the muon collider. These consolidated parameters follow on from the October 2024 Preliminary Parameters Report. Attention has been given to a high-level consistent set of baseline parameters throughout all systems of the complex, following a 10 TeV center-of-mass design. Additional details of the designs contributing to this baseline design are featured in the appendix. Likewise, explorative variations from this baseline set can be found in the appendix. The data is collected from a collaborative spreadsheet and transferred to overleaf.

physics.acc-ph↗

arXiv2025

Deep learning approach for identification of HII regions during reionization in 21-cm observations -- III. image recovery

The low-frequency component of the upcoming Square Kilometre Array Observatory (SKA-Low) will be sensitive enough to construct 3D tomographic images of the 21-cm signal distribution during reionisation. However, foreground contamination poses challenges for detecting this signal, and image recovery will heavily rely on effective mitigation methods. We introduce \texttt{SERENEt}, a deep-learning framework designed to recover the 21-cm signal from SKA-Low's foreground-contaminated observations, enabling the detection of ionised (HII) and neutral (HI) regions during reionisation. \texttt{SERENEt} can recover the signal distribution with an average accuracy of 75 per cent at the early stages ($\overline{x}_\mathrm{HI}\simeq0.9$) and up to 90 per cent at the late stages of reionisation ($\overline{x}_\mathrm{HI}\simeq0.1$). Conversely, HI region detection starts at 92 per cent accuracy, decreasing to 73 per cent as reionisation progresses. Beyond improving image recovery, \texttt{SERENEt} provides cylindrical power spectra with an average accuracy exceeding 93 per cent throughout the reionisation period. We tested \texttt{SERENEt} on a 10-degree field-of-view simulation, consistently achieving better and more stable results when prior maps were provided. Notably, including prior information about HII region locations improved 21-cm signal recovery by approximately 10 per cent. This capability was demonstrated by supplying \texttt{SERENEt} with ionising source distribution measurements, showing that high-redshift galaxy surveys of similar observation fields can optimise foreground mitigation and enhance 21-cm image construction.

astro-ph.CO↗

arXiv2025

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 $σ_8$, 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↗

arXiv2025

The Muon Collider

Muons offer a unique opportunity to build a compact high-energy electroweak collider at the 10 TeV scale. A Muon Collider enables direct access to the underlying simplicity of the Standard Model and unparalleled reach beyond it. It will be a paradigm-shifting tool for particle physics representing the first collider to combine the high-energy reach of a proton collider and the high precision of an electron-positron collider, yielding a physics potential significantly greater than the sum of its individual parts. A high-energy muon collider is the natural next step in the exploration of fundamental physics after the HL-LHC and a natural complement to a future low-energy Higgs factory. Such a facility would significantly broaden the scope of particle colliders, engaging the many frontiers of the high energy community. The last European Strategy for Particle Physics Update and later the Particle Physics Project Prioritisation Panel in the US requested a study of the muon collider, which is being carried on by the International Muon Collider Collaboration. In this comprehensive document we present the physics case, the state of the work on accelerator design and technology, and propose an R\&D project that can make the muon collider a reality.

physics.acc-ph↗

arXiv2025

Karabo: A versatile SKA Observation Simulation Framework

Karabo is a versatile Python-based software framework simplifying research with radio astronomy data. It bundles existing software packages into a coherent whole to improve the ease of use of its components. Karabo includes useful abstractions, like strategies to scale and parallelize typical workloads or science-specific Python modules. The framework includes functionality to access datasets and mock observations to study the Square Kilometer Array (SKA) instruments and their expected accuracy. SKA will address problems in a wide range of fields of astronomy. We demonstrate the application of Karabo to some of the SKA science cases from HI intensity mapping, mock radio surveys, radio source detection, the epoch of re-ionisation and heliophysics. We discuss the capabilities and challenges of simulating large radio datasets in the context of SKA.

astro-ph.IM↗

arXiv2024

BIPP: An efficient HPC implementation of the Bluebild algorithm for radio astronomy

The Bluebild algorithm is a new technique for image synthesis in radio astronomy which decomposes the sky into distinct energy levels using functional principal component analysis. These levels can be linearly combined to construct a least-squares estimate of the radio sky, i.e. minimizing the residuals between measured and predicted visibilities. This approach is particularly useful for deconvolution-free imaging or for scientific applications that need to filter specific energy levels. We present an HPC implementation of the Bluebild algorithm for radio-interferometric imaging: Bluebild Imaging++ (BIPP). The library features interfaces to C++, C and Python and is designed with seamless GPU acceleration in mind. We evaluate the accuracy and performance of BIPP on simulated observations of the upcoming Square Kilometer Array Observatory and real data from the Low-Frequency Array (LOFAR) telescope. We find that BIPP offers accurate wide-field imaging and has competitive execution time with respect to the interferometric imaging libraries CASA and WSClean for images with $\leq 10^6$ pixels. Furthermore, due to the energy level decomposition, images produced with BIPP can reveal information about faint and diffuse structures before any cleaning iterations. BIPP does not perform any regularization, but we suggest methods to integrate the output of BIPP with CLEAN. The source code of BIPP is publicly released.

astro-ph.IM↗

arXiv2024

MuCol Milestone Report No. 5: Preliminary Parameters

This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power consumption of the facility. The data is collected from a collaborative spreadsheet and transferred to overleaf.

physics.acc-ph↗

arXiv2024

The 21-cm signal during the end stages of reionization

During the epoch of reionization (EoR), the 21-cm signal allows direct observation of the neutral hydrogen (HI) in the intergalactic medium (IGM). In the post-reionization era, this signal instead probes HI in galaxies, which traces the dark matter density distribution. With new numerical simulations, we investigated the end stages of reionization to elucidate the transition of our Universe into the post-reionization era. Our models are consistent with the latest high-redshift measurements, including ultraviolet (UV) luminosity functions \RefereeReport{up to redshift $\simeq$8}. Notably, these models consistently reproduced the evolution of the UV photon background, which is constrained from Lyman-$α$ absorption spectra. We studied the dependence of this background on the nature of photon sinks in the IGM, requiring mean free path of UV photons to be $\sim$10 comoving-megaparsecs (cMpc) during the EoR that increases gradually with time during late stages ($z\lesssim 6$). Our models revealed that the reionization of the IGM transitioned from an \textit{inside-out} to an \textit{outside-in} process when the Universe is less than 0.01 per cent neutral. During this epoch, the 21-cm signal also shifted from probing predominantly the HI in the IGM to that in galaxies. Furthermore, we identified a statistically significant number of large neutral islands (with sizes up to 40 cMpc) persisting until very late stages ($5 \lesssim z \lesssim 6$) that can imprint features in Lyman-$α$ absorption spectra and also produce a knee-like feature in the 21-cm power spectrum.

astro-ph.CO↗

arXiv2024

pyC$^2$Ray: A flexible and GPU-accelerated Radiative Transfer Framework for Simulating the Cosmic Epoch of Reionization

Detailed modeling of the evolution of neutral hydrogen in the intergalactic medium during the Epoch of Reionization, $5 \leq z \leq 20$, is critical in interpreting the cosmological signals from current and upcoming 21-cm experiments such as the Low-Frequency Array (LOFAR) and the Square Kilometre Array (SKA). Numerical radiative transfer codes provide the most physically accurate models of the reionization process. However, they are computationally expensive as they must encompass enormous cosmological volumes while accurately capturing astrophysical processes occurring at small scales ($\lesssim\rm Mpc$). Here, we present pyC$^2$Ray, an updated version of the massively parallel ray-tracing and chemistry code, C$^2$Ray, which has been extensively employed in reionization simulations. The most time-consuming part of the code is calculating the hydrogen column density along the path of the ionizing photons. Here, we present the Accelerated Short-characteristics Octahedral ray-tracing (ASORA) method, a ray-tracing algorithm specifically designed to run on graphical processing units (GPUs). We include a modern Python interface, allowing easy and customized use of the code without compromising computational efficiency. We test pyC$^2$Ray on a series of standard ray-tracing tests and a complete cosmological simulation with volume size $(349\,\rm Mpc)^3$, mesh size of $250^3$ and approximately $10^6$ sources. Compared to the original code, pyC$^2$Ray achieves the same results with negligible fractional differences, $\sim 10^{-5}$, and a speedup factor of two orders of magnitude. Benchmark analysis shows that ASORA takes a few nanoseconds per source per voxel and scales linearly for an increasing number of sources and voxels within the ray-tracing radii.

astro-ph.CO↗

arXiv2024

Deep learning approach for identification of HII regions during reionization in 21-cm observations -- II. foreground contamination

The upcoming Square Kilometre Array Observatory (SKAO) will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as noise and galactic foreground contamination which pose a challenge for accurate detection. In this study, we present the SegU-Net v2 framework, an enhanced version of our convolutional neural network, built to identify neutral and ionized regions in the 21-cm signal contaminated with foreground emission. We trained our neural network on 21-cm image data processed by a foreground removal method based on Principal Component Analysis achieving an average classification accuracy of 71 per cent between redshift $z=7$ to $11$. We tested SegU-Net v2 against various foreground removal methods, including Gaussian Process Regression, Polynomial Fitting, and Foreground-Wedge Removal. Results show comparable performance, highlighting SegU-Net v2's independence on these pre-processing methods. Statistical analysis shows that a perfect classification score with $AUC=95\%$ is possible for $8 (10\, {\rm cMpc})^3$ at $z>9$, for follow-up studies with infrared/optical telescopes to detect these sources.

astro-ph.IM↗