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Fabio Vitello

Publications and source records attributed to Fabio Vitello.

16 recordsLinked to original sources
arXiv2026

iDaVIE v1.0: A virtual reality tool for interactive analysis of astronomical data cubes

As modern astronomy confronts unprecedented data volumes, automated pipelines and machine-learning techniques have become essential for processing and analysis. As these workflows grow more complex, astronomers also require input and inspection tools that can keep pace. To address challenges in navigating multidimensional datasets for quality control and scientific interpretation, we present the immersive Data Visualisation Interactive Explorer (iDaVIE), a virtual reality (VR) software suite developed in collaboration with the astronomy community. iDaVIE enables users to import and render large 3D data cubes within a VR environment, offering real-time tools for selection, cropping, catalogue overlays, and exporting results back into existing pipelines. Built on the Unity engine and SteamVR, the system uses custom plug-ins for efficient data parsing, downsampling, and statistical calculations. The software has already been integrated into workflows such as verifying HI data cubes from MeerKAT, ASKAP, and APERTIF, refining detection masks, and identifying new sources. Its intuitive interface aims to reduce the cognitive load associated with higher-dimensional data, allowing researchers to focus more directly on scientific goals. As an open-source, scalable, and adaptable platform, iDaVIE supports continued development and integration with other tools. Version 1.0 marks a significant milestone, with planned enhancements including subcube loading, advanced rendering modes, video-generation scripts, and collaborative capabilities. By pairing immersive visualisation with robust interaction tools, iDaVIE seeks to transform how researchers engage with complex datasets and enhance productivity in the era of big data.

astro-ph.IM↗
arXiv2026

Developing a Portable Solution for Post-Event Analysis Pipelines

In recent years, the monitoring and study of natural hazards have gained significant attention, particularly due to climate change, which exacerbates incidents like floods, droughts, storm surges, and landslides. Together with the constant risk of earthquakes, these climate-induced events highlight the critical necessity for enhanced risk assessment and mitigation strategies in susceptible areas such as Italy. In this work, we present a Science Gateway framework for the development of portable and fully automated post-event analysis pipelines integrating Photogrammetry techniques, Data Visualization and Artificial Intelligence technologies, applied on aerial images, to assess extreme natural events and evaluate their impact on risk-exposed assets.

cs.DC↗
arXiv2025

High performance visualization for Astronomy and Cosmology: the VisIVO's pathway toward Exascale systems

Petabyte-scale data volumes are generated by observations and simulations in modern astronomy and astrophysics. Storage, access, and data analysis are significantly hampered by such data volumes and are leading to the development of a new generation of software tools. The Visualization Interface for the Virtual Observatory (VisIVO) has been designed, developed and maintained by INAF since 2005 to perform multi-dimensional data analysis and knowledge discovery in multivariate astrophysical datasets. Utilizing containerization and virtualization technologies, VisIVO has already been used to exploit distributed computing infrastructures including the European Open Science Cloud (EOSC). We intend to adapt VisIVO solutions for high performance visualization of data generated on the (pre-)Exascale systems by HPC applications in Astrophysics and Cosmology (A\&C), including GADGET (GAlaxies with Dark matter and Gas) and PLUTO simulations, thanks to the collaboration within the SPACE Center of Excellence, the H2020 EUPEX Project, and the ICSC National Research Centre. In this work, we outline the evolution's course as well as the execution strategies designed to achieve the following goals: enhance the portability of the VisIVO modular applications and their resource requirements; foster reproducibility and maintainability; take advantage of a more flexible resource exploitation over heterogeneous HPC facilities; and, finally, minimize data-movement overheads and improve I/O performances.

astro-ph.IM↗
arXiv2025

From Local to Remote: VisIVO Visual Analytics in the Era of the Square Kilometre Array

The field of astrophysics is continuously advancing, with an ever-growing influx of data requiring robust and efficient analysis tools. As the Square Kilometre Array (SKA) radio telescopes come fully operational, we anticipate the generation of hundreds of petabytes of data annually, characterized by unprecedented resolution and detail. In this context, scientific visualization becomes a critical component, enabling researchers to interpret complex datasets and extract meaningful insights. The immense volume of data demands not only suitable tools but also substantial infrastructure and computational capacity to analyze it effectively. In this work, we will discuss how we are addressing these challenges with the development of our interactive visualization tool named VisIVO Visual Analytics. The tool is transitioning from a local visualizer to a remote visualizer, utilizing a client-server architecture. This evolution will allow the software to run parallel visualization pipelines on high-performance computing (HPC) clusters, thereby enhancing its capacity to handle extensive datasets efficiently.

astro-ph.IM↗
arXiv2024

Self-supervised learning for radio-astronomy source classification: a benchmark

The upcoming Square Kilometer Array (SKA) telescope marks a significant step forward in radio astronomy, presenting new opportunities and challenges for data analysis. Traditional visual models pretrained on optical photography images may not perform optimally on radio interferometry images, which have distinct visual characteristics. Self-Supervised Learning (SSL) offers a promising approach to address this issue, leveraging the abundant unlabeled data in radio astronomy to train neural networks that learn useful representations from radio images. This study explores the application of SSL to radio astronomy, comparing the performance of SSL-trained models with that of traditional models pretrained on natural images, evaluating the importance of data curation for SSL, and assessing the potential benefits of self-supervision to different domain-specific radio astronomy datasets. Our results indicate that, SSL-trained models achieve significant improvements over the baseline in several downstream tasks, especially in the linear evaluation setting; when the entire backbone is fine-tuned, the benefits of SSL are less evident but still outperform pretraining. These findings suggest that SSL can play a valuable role in efficiently enhancing the analysis of radio astronomical data. The trained models and code is available at: \url{https://github.com/dr4thmos/solo-learn-radio}

astro-ph.IM↗
arXiv2023

Radio source analysis services for the SKA and precursors

New developments in data processing and visualization are being made in preparation for upcoming radioastronomical surveys planned with the Square Kilometre Array (SKA) and its precursors. A major goal is enabling extraction of science information from the data in a mostly automated way, possibly exploiting the capabilities offered by modern computing infrastructures and technologies. In this context, the integration of source analysis algorithms into data visualization tools is expected to significantly improve and speed up the cataloguing process of large area surveys. To this aim, the CIRASA (Collaborative and Integrated platform for Radio Astronomical Source Analysis) project was recently started to develop and integrate a set of services for source extraction, classification and analysis into the ViaLactea visual analytic platform and knowledge base archive. In this contribution, we will present the project objectives and tools that have been developed, interfaced and deployed so far on the prototype European Open Science Cloud (EOSC) infrastructure provided by the H2020 NEANIAS project.

astro-ph.IM↗
arXiv2022

Spectral study of the diffuse synchrotron source in the galaxy cluster Abell 523

The galaxy cluster Abell 523 (A523) hosts an extended diffuse synchrotron source historically classified as a radio halo. Its radio power at 1.4 GHz makes it one of the most significant outliers in the scaling relations between observables derived from multi-wavelength observations of galaxy clusters: it has a morphology that is different and offset from the thermal gas, and it has polarized emission at 1.4 GHz typically difficult to observe for this class of sources. A magnetic field fluctuating on large spatial scales (~ 1 Mpc) can explain these peculiarities but the formation mechanism for this source is not yet completely clear. To investigate its formation mechanism, we present new observations obtained with the LOw Frequency ARray at 120-168 MHz and the Jansky Very Large Array at 1-2 GHz, which allow us to study the spectral index distribution of this source. According to our data the source is observed to be more extended at 144 MHz than previously inferred at 1.4 GHz, with a total size of about 1.8 Mpc and a flux density S_144MHz = (1.52 +- 0.31) Jy. The spectral index distribution of the source is patchy with an average spectral index alpha ~ 1.2 between 144 MHz and 1.410 GHz, while an integrated spectral index alpha ~ 2.1 has been obtained between 1.410 GHz and 1.782 GHz. A previously unseen patch of steep spectrum emission is clearly detected at 144 MHz in the south of the cluster. Overall, our findings suggest that we are observing an overlapping of different structures, powered by the turbulence associated with the primary and a possible secondary merger.

astro-ph.CO↗
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.

astro-ph.IM↗
arXiv2021

LOgging UnifieD for ASTRI Mini Array

The ASTRI (Astrofisica con Specchi a Tecnologia Replicante Italiana) Mini-Array (MA) project is an international collaboration led by the Italian National Institute for Astrophysics (INAF). ASTRI MA is composed of nine Cherenkov telescopes operating in the energy range 1-100 TeV, and it aims to study very high-energy gamma ray astrophysics and optical intensity interferometry of bright stars. ASTRI MA is currently under construction, and will be installed at the site of the Teide Observatory in Tenerife (Spain). The hardware and software system that is responsible of monitoring and controlling all the operations carried out at the ASTRI MA site is the Supervision Control and Data Acquisition (SCADA). The LOgging UnifieD (LOUD) subsystem is one of the main components of SCADA. It provides the service responsible for collecting, filtering, exposing and storing log events collected by all the array elements (telescopes, LIDAR, devices, etc.). In this paper, we present the LOUD architecture and the software stack explicitly designed for distributed computing environments exploiting Internet of Things technologies (IoT).

astro-ph.IM↗
arXiv2021

Novel EOSC Services for Space Challenges: The NEANIAS First Outcomes

The European Open Science Cloud (EOSC) initiative faces the challenge of developing an agile, fit-for-purpose, and sustainable service-oriented platform that can address the evolving needs of scientific communities. The NEANIAS project plays an active role in the materialization of the EOSC ecosystem by actively contributing to the technological, procedural, strategic and business development of EOSC. We present the first outcomes of the NEANIAS activities relating to co-design and delivery of new innovative services for space research for data management and visualization (SPACE-VIS), map making and mosaicing (SPACE-MOS) and pattern and structure detection (SPACE-ML). We include a summary of collected user requirements driving our services and methodology for their delivery, together with service access details and pointers to future works.

astro-ph.IM↗
arXiv2020

iDaVIE-v: immersive Data Visualisation Interactive Explorer for volumetric rendering

We present the beta release of iDaVIE-v, a new Virtual Reality software for data cube exploration. The beta release of iDaVIE-v (immersive Data Visualisation Interactive Explorer for volumetric rendering) is planned for release in early 2021. iDaVIE-v has been developed through the Unity game engine using the SteamVR plugin and is compatible with all commercial headsets. It allows the visualization, exploration and interaction of data for scientific analysis. Originally developed to serve the HI Radio Astronomy community for HI source identification, the software has now completed the alpha testing phase and is already showing capabilities that will serve the broader astronomical community and more. iDaVIE-v has been developed at the IDIA Visualisation Lab (IVL) based at the University of Cape Town in collaboration with the Italian National Institute for Astrophysics (INAF) in Catania.

astro-ph.IM↗
arXiv2020

Toward porting Astrophysics Visual Analytics Services to the European Open Science Cloud

The European Open Science Cloud (EOSC) aims to create a federated environment for hosting and processing research data to support science in all disciplines without geographical boundaries, such that data, software, methods and publications can be shared as part of an Open Science community of practice. This work presents the ongoing activities related to the implementation of visual analytics services, integrated into EOSC, towards addressing the diverse astrophysics user communities needs. These services rely on visualisation to manage the data life cycle process under FAIR principles, integrating data processing for imaging and multidimensional map creation and mosaicing, and applying machine learning techniques for detection of structures in large scale multidimensional maps.

astro-ph.IM↗
arXiv2020

CHIPP: INAF pilot project for HTC, HPC and HPDA

CHIPP (Computing HTC in INAF Pilot Project) is an Italian project funded by the Italian Institute for Astrophysics (INAF) and promoted by the ICT office of INAF. The main purpose of the CHIPP project is to coordinate the use of, and access to, already existing high throughput computing and high-performance computing and data processing resources (for small/medium size programs) for the INAF community. Today, Tier2/Tier3 systems (1,200 CPU/core) are provided at the INAF institutes at Trieste and Catania, but in the future, the project will evolve including also other computing infrastructures. During the last two years, more than 30 programs have been approved for a total request of 30 Million CPU-h. Most of the programs are HPC, data reduction and analysis, machine learning. In this paper, we describe in details the CHIPP infrastructures and the results of the first two years of activity.

astro-ph.IM↗
arXiv2016

VIALACTEA knowledge base homogenizing access to Milky Way data

The VIALACTEA project has a work package dedicated to Tools and Infrastructure and, inside it, a task for the Database and Virtual Observatory Infrastructure. This task aims at providing an infrastructure to store all the resources needed by the, more purposely, scientific work packages of the project itself. This infrastructure includes a combination of: storage facilities, relational databases and web services on top of them, and has taken, as a whole, the name of VIALACTEA Knowledge Base (VLKB). This contribution illustrates the current status of this VLKB. It details the set of data resources put together; describes the database that allows data discovery through VO inspired metadata maintenance; illustrates the discovery, cutout and access services built on top of the former two for the users to exploit the data content.

astro-ph.IM↗
arXiv2016

An Innovative Workspace for The Cherenkov Telescope Array

The Cherenkov Telescope Array (CTA) is an initiative to build the next generation, ground-based gamma-ray observatories. We present a prototype workspace developed at INAF that aims at providing innovative solutions for the CTA community. The workspace leverages open source technologies providing web access to a set of tools widely used by the CTA community. Two different user interaction models, connected to an authentication and authorization infrastructure, have been implemented in this workspace. The first one is a workflow management system accessed via a science gateway (based on the Liferay platform) and the second one is an interactive virtual desktop environment. The integrated workflow system allows to run applications used in astronomy and physics researches into distributed computing infrastructures (ranging from clusters to grids and clouds). The interactive desktop environment allows to use many software packages without any installation on local desktops exploiting their native graphical user interfaces. The science gateway and the interactive desktop environment are connected to the authentication and authorization infrastructure composed by a Shibboleth identity provider and a Grouper authorization solution. The Grouper released attributes are consumed by the science gateway to authorize the access to specific web resources and the role management mechanism in Liferay provides the attribute-role mapping.

astro-ph.IM↗