arXiv subjects

Claudio Gheller

Publications and source records attributed to Claudio Gheller.

At least 19 recordsLinked to original sources

arXiv2026

Simulating megaparsec-scale jets of radio galaxies: Magneto-hydrodynamics of jets reaching 5 Mpc

Extragalactic jets have long prompted the question of how far relativistic outflows can extend, with some radio sources reaching 5 - 7 Mpc in length. These great extents motivate investigations into their ages, propagation dynamics, stability, and impact on the environment. We perform 3D high-resolution numerical simulations of two jet configurations involving continuous injection at different powers propagating in low-density regions of the cosmos (static and laminar), investigating the conditions for jet collimation versus disruption at extreme scales. We show that the combined effects of higher jet thrust (enhanced kinetic power), improved collimation (suppression of transverse distortions), and magnetic stabilization (strengthened poloidal field) can sustain a laterally confined flow, enabling such a jet to reach 5 Mpc in just 15 Myr (injecting a total energy of $2.3 \times 10^{61}$ erg into the environment). In contrast, a jet lacking these conditions dissipates more rapidly, forming lobe-like morphologies and reaching only $\sim 3$ Mpc over $\sim35$ Myr (injecting total energy of $8.1 \times 10^{60}$ erg). Pinch and kink MHD instabilities are identified as the primary drivers of transverse distortions; their suppression allows the persistence of a fast spine alongside a slower, dissipative head (location of maximum environmental interaction). We find that the jet-head propagation shows two regimes: one with speed $\sim0.5 c$; the other with speed from $\sim 0.2 c$ to $\sim 0.05 c$. We consider a proxy of synchrotron emission and find that radiation is concentrated in regions of enhanced compression and magnetic amplification, primarily near the first recollimation shock (producing a bright radio spot) and at the jet-head interaction zone (producing the radio termination lobe). Such jets facilitate the transport of substantial energy and magnetic flux into underdense cosmic regions.

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arXiv2026

Cosmological simulation of a radio synchrotron bridge between pre-merging galaxy clusters

Radio bridges are diffuse synchrotron emission observed between merging galaxy clusters. Recent radio observations have reported both detections and non-detections of radio bridges between clusters. The detections imply the presence of cosmic rays (CRs) and magnetic fields permeating the cosmic web that produce synchrotron emission observable with current facilities, whereas the non-detections suggest that specific physical conditions are required for their formation. We study the CR reacceleration by solenoidal turbulence in the filament connecting two massive clusters at an early stage of the merger. Our aim is to test whether this mechanism can generate diffuse emission in the inter-cluster region. We perform a cosmological magneto-hydrodynamical (MHD) simulation using the Enzo code. We improved a run-time Lagrangian tracer method implemented in Enzo, and follow the trajectories of baryonic matter using $N=\mathcal{O}(10^7)$ tracer particles. In post-processing, we conduct a parallel computation of the Fokker-Planck (FP) equation for all tracers, with cooling and reacceleration efficiencies evaluated from the local quantities recorded along each tracer trajectory. Our simulation generate a Mpc-sized radio bridge in the early stage of the cluster merger. Within a reasonable parameter range, the reacceleration model produces a broad variety of spectra. In our fiducial model, the simulated bridge matches several properties of the one found between Abell 399 and Abell 401, such as its spectral shape, intensity profile, and pixel-by-pixel correlation between radio and X-ray intensities. The inter-cluster region is filled with turbulence induced by infalling mass clumps and subsequently amplified by the approaching motion of the clusters. The CR reacceleration by the turbulence is a viable mechanism to power a Mpc-sized synchrotron emission observed as radio bridges.

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arXiv2026

Accelerating radio astronomy imaging with RICK: a step towards SKA-Mid and SKA-Low

The data volumes generated by modern radio interferometers, such as the SKA precursors, present significant computational challenges for imaging pipelines. Addressing the need for high-performance, portable, and scalable software, we present RICK 2.0 (Radio Imaging Code Kernels). This work introduces a novel implementation that leverages the HeFFTe library for distributed Fast Fourier Transforms, ensuring portability across diverse HPC architectures, including multi-core CPUs and accelerators. We validate RICK's correctness and performance against real observational data from both MeerKAT and LOFAR. Our results demonstrate that the HeFFTe-based implementation offers substantial performance advantages, particularly when running on GPUs, and scales effectively with large pixel resolutions and a high number of frequency planes. This new architecture overcomes the critical scaling limitations identified in previous work (Paper II, Paper III), where communication overheads consumed up to 96% of the runtime due to the necessity of communicating the entire grid. This new RICK version drastically reduces this communication impact, representing a scalable and efficient imaging solution ready for the SKA era.

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arXiv2025

Estimating Flux Densities of Diffuse Cosmological Radio Sources Exploiting Vision Transformers

We present TUNA, a Vision-Transformer based network adapted from segmentation to flux regression for faint, diffuse radio emission. Trained on LOFAR-like mock observations derived from cosmological simulations, TUNA accurately reconstructs low surface-brightness structures, with only mild smoothing and small brightness-dependent biases. Applied to LOFAR data of the A399 - A401 galaxy cluster system, it recovers the ridge not identifiable in the high resolution observation and matches the low resolution tapered map. These results indicate how TUNA can deliver automated, quantitative surface brightness estimates for diffuse extragalactic sources, enabling scalable analyses for upcoming surveys.

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arXiv2025

Mapping Diffuse Radio Sources Using TUNA: A Transformer-Based Deep Learning Approach

Vision Transformers are used via a customized TransUNet architecture, which is a hybrid model combining Transformers into a U-Net backbone, to achieve precise, automated, and fast segmentation of radio astronomy data affected by calibration and imaging artifacts, addressing the identification of faint, diffuse radio sources. Trained on mock radio observations from numerical simulations, the network is applied to the LOFAR Two-meter Sky Survey data. It is then evaluated on key use cases, specifically megahalos and bridges between galaxy clusters, to assess its performance in targeting sources at different resolutions and at the sensitivity limits of the telescope. The network is capable of detecting low surface brightness radio emission without manual source subtraction or re-imaging. The results demonstrate its groundbreaking capability to identify sources that typically require reprocessing at resolutions 4-6 times lower than that of the input image, accurately capturing their morphology and ensuring detection completeness. This approach represents a significant advancement in accelerating discovery within the large datasets generated by next-generation radio telescopes.

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arXiv2025

Green computing toward SKA era with RICK

Square Kilometer Array is expected to generate hundreds of petabytes of data per year, two orders of magnitude more than current radio interferometers. Data processing at this scale necessitates advanced High Performance Computing (HPC) resources. However, modern HPC platforms consume up to tens of M W , i.e. megawatts, and energy-to-solution in algorithms will become of utmost importance in the next future. In this work we study the trade-off between energy-to-solution and time-to-solution of our RICK code (Radio Imaging Code Kernels), which is a novel approach to implement the w-stacking algorithm designed to run on state-of-the-art HPC systems. The code can run on heterogeneous systems exploiting the accelerators. We did both single-node tests and multi-node tests with both CPU and GPU solutions, in order to study which one is the greenest and which one is the fastest. We then defined the green productivity, i.e. a quantity which relates energy-to-solution and time-to-solution in different code configurations compared to a reference one. Configurations with the highest green productivities are the most efficient ones. The tests have been run on the Setonix machine available at the Pawsey Supercomputing Research Centre (PSC) in Perth (WA), ranked as 28th in Top500 list, updated at June 2024.

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arXiv2024

Accelerating radio astronomy imaging with RICK

This paper presents an implementation of radio astronomy imaging algorithms on modern High Performance Computing (HPC) infrastructures, exploiting distributed memory parallelism and acceleration throughout multiple GPUs. Our code, called RICK (Radio Imaging Code Kernels), is capable of performing the major steps of the w-stacking algorithm presented in Offringa et al. (2014) both inter- and intra-node, and in particular has the possibility to run entirely on the GPU memory, minimising the number of data transfers between CPU and GPU. This feature, especially among multiple GPUs, is critical given the huge sizes of radio datasets involved. After a detailed description of the new implementations of the code with respect to the first version presented in Gheller et al. (2023), we analyse the performances of the code for each step involved in its execution. We also discuss the pros and cons related to an accelerated approach to this problem and its impact on the overall behaviour of the code. Such approach to the problem results in a significant improvement in terms of runtime with respect to the CPU version of the code, as long as the amount of computational resources does not exceed the one requested by the size of the problem: the code, in fact, is now limited by the communication costs, with the computation that gets heavily reduced by the capabilities of the accelerators.

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arXiv2024

Radio U-Net: a convolutional neural network to detect diffuse radio sources in galaxy clusters and beyond

The forthcoming generation of radio telescope arrays promises significant advancements in sensitivity and resolution, enabling the identification and characterization of many new faint and diffuse radio sources. Conventional manual cataloging methodologies are anticipated to be insufficient to exploit the capabilities of new radio surveys. Radio interferometric images of diffuse sources present a challenge for image segmentation tasks due to noise, artifacts, and embedded radio sources. In response to these challenges, we introduce Radio U-Net, a fully convolutional neural network based on the U-Net architecture. Radio U-Net is designed to detect faint and extended sources in radio surveys, such as radio halos, relics, and cosmic web filaments. Radio U-Net was trained on synthetic radio observations built upon cosmological simulations and then tested on a sample of galaxy clusters, where the detection of cluster diffuse radio sources relied on customized data reduction and visual inspection of LOFAR Two Metre Sky Survey (LoTSS) data. The 83% of clusters exhibiting diffuse radio emission were accurately identified, and the segmentation successfully recovered the morphology of the sources even in low-quality images. In a test sample comprising 246 galaxy clusters, we achieved a 73% accuracy rate in distinguishing between clusters with and without diffuse radio emission. Our results establish the applicability of Radio U-Net to extensive radio survey datasets, probing its efficiency on cutting-edge high-performance computing systems. This approach represents an advancement in optimizing the exploitation of forthcoming large radio surveys for scientific exploration.

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arXiv2023

Efficiency of turbulent reacceleration by solenoidal turbulence and its application to the origin of radio mega halos in cluster outskirts

Recent radio observations with Low-Frequency Array (LOFAR) discovered diffuse emission extending beyond the scale of classical radio halos. The presence of such mega halos indicates that the amplification of the magnetic field and acceleration of relativistic particles are working in the cluster outskirts, presumably due to the combination of shocks and turbulence that dissipate energy in these regions. Cosmological magnetohydrodynamical (MHD) simulations of galaxy clusters suggest that solenoidal turbulence has a significant energy budget in the outskirts of galaxy clusters. In this paper, we explore the possibility that this turbulence contributes to the emission observed in mega halos through second-order Fermi acceleration of relativistic particles and the magnetic field amplification by the dynamo. We focus on the case of Abell 2255 and find that this scenario can explain the basic properties of the diffuse emission component that is observed under assumptions that are used in previous literature. More specifically, we conduct a numerical follow-up, solving the Fokker--Planck equation using a snapshot of a MHD simulation and deducing the synchrotron brightness integrated along the lines of sight. We find that a volume-filling emission, ranging between 30 and almost 100% of the projected area depending on our assumptions on the particle diffusion and transport, can be detected at LOFAR sensitivities. Assuming a magnetic field $B\sim0.2\mu$G, as derived from a dynamo model applied to the emitting region, we find that the observed brightness can be matched when $\sim$1% level of the solenoidal turbulent energy flux is channeled into particle acceleration.

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arXiv2023

High Performance W-stacking for Imaging Radio Astronomy Data: a Parallel and Accelerated Solution

Current and upcoming radio-interferometers are expected to produce volumes of data of increasing size that need to be processed in order to generate the corresponding sky brightness distributions through imaging. This represents an outstanding computational challenge, especially when large fields of view and/or high resolution observations are processed. We have investigated the adoption of modern High Performance Computing systems specifically addressing the gridding, FFT-transform and w-correction of imaging, combining parallel and accelerated solutions. We have demonstrated that the code we have developed can support dataset and images of any size compatible with the available hardware, efficiently scaling up to thousands of cores or hundreds of GPUs, keeping the time to solution below one hour even when images of the size of the order of billion or tens of billion of pixels are generated. In addition, portability has been targeted as a primary objective, both in terms of usability on different computing platforms and in terms of performance. The presented results have been obtained on two different state-of-the-art High Performance Computing architectures.

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arXiv2022

Rosetta: a container-centric science platform for resource-intensive, interactive data analysis

Rosetta is a science platform for resource-intensive, interactive data analysis which runs user tasks as software containers. It is built on top of a novel architecture based on framing user tasks as microservices - independent and self-contained units - which allows to fully support custom and user-defined software packages, libraries and environments. These include complete remote desktop and GUI applications, besides common analysis environments as the Jupyter Notebooks. Rosetta relies on Open Container Initiative containers, which allow for safe, effective and reproducible code execution; can use a number of container engines and runtimes; and seamlessly supports several workload management systems, thus enabling containerized workloads on a wide range of computing resources. Although developed in the astronomy and astrophysics space, Rosetta can virtually support any science and technology domain where resource-intensive, interactive data analysis is required.

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arXiv2022

A distributed computing infrastructure for LOFAR Italian community

The LOw-Frequency ARray is a low-frequency radio interferometer composed by observational stations spread across Europe and it is the largest precursor of SKA in terms of effective area and generated data rates. In 2018, the Italian community officially joined LOFAR project, and it deployed a distributed computing and storage infrastructure dedicated to LOFAR data analysis. The infrastructure is based on 4 nodes distributed in different Italian locations and it offers services for pipelines execution, storage of final and intermediate results and support for the use of the software and infrastructure. As the analysis of the LOw-Frequency ARray data requires a very complex computational procedure, a container-based approach has been adopted to distribute software environments to the different computing resources. A science platform approach is used to facilitate interactive access to computational resources. In this paper, we describe the architecture and main features of the infrastructure.

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arXiv2021

Magnetogenesis and the Cosmic Web: a joint challenge for radio observations and numerical simulations

The detection of the radio signal from filaments in the cosmic web is crucial to distinguish possible magnetogenesis scenarios. We review the status of the different attempts to detect the cosmic web at radio wavelengths. This is put into the context of the advanced simulations of cosmic magnetism carried out in the last few years by our {\magcow} project. While first attempts of imaging the cosmic web with the MWA and LOFAR have been encouraging and could discard some magnetogenesis models, the complexity behind such observations makes a definitive answer still uncertain. A combination of total intensity and polarimetric data at low radio frequencies that the SKA and LOFAR2.0 will achieve is key to removing the existing uncertainties related to the contribution of many possible sources of signal along deep lines of sight. This will make it possible to isolate the contribution from filaments, and expose its deep physical connection with the origin of extragalactic magnetism.

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arXiv2021

Convolutional Deep Denoising Autoencoders for Radio Astronomical Images

We apply a Machine Learning technique known as Convolutional Denoising Autoencoder to denoise synthetic images of state-of-the-art radio telescopes, with the goal of detecting the faint, diffused radio sources predicted to characterise the radio cosmic web. In our application, denoising is intended to address both the reduction of random instrumental noise and the minimisation of additional spurious artefacts like the sidelobes, resulting from the aperture synthesis technique. The effectiveness and the accuracy of the method are analysed for different kinds of corrupted input images, together with its computational performance. Specific attention has been devoted to create realistic mock observations for the training, exploiting the outcomes of cosmological numerical simulations, to generate images corresponding to LOFAR HBA 8 hours observations at 150 MHz. Our autoencoder can effectively denoise complex images identifying and extracting faint objects at the limits of the instrumental sensitivity. The method can efficiently scale on large datasets, exploiting high performance computing solutions, in a fully automated way (i.e. no human supervision is required after training). It can accurately perform image segmentation, identifying low brightness outskirts of diffused sources, proving to be a viable solution for detecting challenging extended objects hidden in noisy radio observations.

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arXiv2021

On the alignment of haloes, filaments and magnetic fields in the simulated cosmic web

The continuous flow of gas and dark matter across scales in the cosmic web can generate correlated dynamical properties of haloes and filaments (and the magnetic fields they contain). With this work, we study the halo spin properties and orientation with respect to filaments, and the morphology of the magnetic field around these objects, for haloes with masses in the range 1e8-1e14 Msun and filaments up to 8 Mpc long. Furthermore, we study how these properties vary in presence, or lack thereof, of different (astro)physical processes and with different magnetic initial conditions. We perform cosmological magnetohydrodynamical simulations with the Eulerian code Enzo and we develop a simple and robust algorithm to study the filamentary connectivity of haloes in three dimensions. We investigate the morphological and magnetic properties and focus on the alignment of the magnetic field along filaments: our analysis suggests that the degree of this alignment is partially dependent on the physical processes involved, as well as on magnetic initial conditions. We discuss the contribution of this effect on a potential attempt to detect the magnetic field surrounding these objects: we find that it introduces a bias in the estimation of the magnetic field from Faraday rotation measure techniques. Specifically, given the strong tendency we find for extragalactic magnetic fields to align with the filaments axis, the value of the magnetic field can be underestimated by a factor 3, because this effect contributes to making the line-of-sight magnetic field (for filaments in the plane of the sky) much smaller than the total one.

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arXiv2021

A New View of Observed Galaxies through 3D Modelling and Visualisation

Observational astronomers survey the sky in great detail to gain a better understanding of many types of astronomical phenomena. In particular, the formation and evolution of galaxies, including our own, is a wide field of research. Three dimensional (spatial 3D) scientific visualisation is typically limited to simulated galaxies, due to the inherently two dimensional spatial resolution of Earth-based observations. However, with appropriate means of reconstruction, such visualisation can also be used to bring out the inherent 3D structure that exists in 2D observations of known galaxies, providing new views of these galaxies and visually illustrating the spatial relationships within galaxy groups that are not obvious in 2D. We present a novel approach to reconstruct and visualise 3D representations of nearby galaxies based on observational data using the scientific visualisation software Splotch. We apply our approach to a case study of the nearby barred spiral galaxy known as M83, presenting a new perspective of the M83 local group and highlighting the similarities between our reconstructed views of M83 and other known galaxies of similar inclinations.

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arXiv2021

New constraints on the magnetic field in filaments of the cosmic web

Strong accretion shocks are expected to illuminate the warm-hot inter-galactic medium encompassed by the filaments of the cosmic web, through synchrotron radio emission. Given their high sensitivity, low-frequency large radio facilities may already be able to detect signatures of this extended radio emission from the region in between two close and massive galaxy clusters. In this work we exploit the non-detection of such diffuse emission by deep observations of two pairs of relatively close ($\simeq 10$ Mpc) and massive ($M_{500}\geq 10^{14}M_\odot$) galaxy clusters using the LOw-Frequency ARray (LOFAR). By combining the results from the two putative inter-cluster filaments, we derive new independent constraints on the median strength of inter-galactic magnetic fields: $B_{\rm 10 Mpc}< 2.5\times 10^2\,\rm nG\,(95\%\, \rm CL)$. Based on cosmological simulations and assuming a primordial origin of the B-fields, these estimates can be used to limit the amplitude of primordial seed magnetic fields: $B_0\leq10\,\rm nG$. We advise the observation of similar cluster pairs as a powerful tool to set tight constraints on the amplitude of extragalactic magnetic fields.

astro-ph.CO↗

arXiv2020

Multi wavelength cross-correlation analysis of the simulated cosmic web

We used magneto-hydrodynamical cosmological simulations to investigate the cross-correlation between different observables (i.e. X-ray emission, Sunyaev-Zeldovich signal at 21 cm, HI temperature decrement, diffuse synchrotron emission and Faraday Rotation) as a probe of the diffuse matter distribution in the cosmic web. We adopt an uniform and simplistic approach to produce synthetic observations at various wavelengths, and we compare the detection chances of different combinations of observables correlated with each other and with the underlying galaxy distribution in the volume. With presently available surveys of galaxies and existing instruments, the best chances to detect the diffuse gas in the cosmic web outside of halos is by cross-correlating the distribution of galaxies with Sunyaev-Zeldovich observations. We also find that the cross-correlation between the galaxy network and the radio emission or the Faraday Rotation can already be used to limit the amplitude of extragalactic magnetic fields, well outside of the cluster volume usually explored by existing radio observations, and to probe the origin of cosmic magnetism with the future generation of radio surveys.

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