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Ugo Becciani

Publications and source records attributed to Ugo Becciani.

At least 19 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

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↗
arXiv2025

The Gaia AVU-GSR solver: a CPU + GPU parallel code toward Exascale systems

The solver module of the Astrometric Verification Unit - Global Sphere Reconstruction (AVU-GSR) pipeline aims to find the astrometric parameters of $\sim$$10^8$ stars in the Milky Way, besides the attitude and instrumental settings of the Gaia satellite and the parametrized post Newtonian parameter $\gamma$ with a resolution of 10-100 micro-arcseconds. To perform this task, the code solves a system of linear equations with the iterative Least Squares (LSQR) algorithm, where the coefficient matrix is large (10-50 TB) and sparse and the iterations stop when convergence is reached in the least squares sense. The two matrix-by-vector products performed at each LSQR step were GPU-ported, firstly with OpenACC and then with CUDA, resulting in a $\sim$$1.5$x and $\sim$$14$x speedup, respectively, over an original code version entirely parallelized on the CPU with MPI + OpenMP. The CUDA code was further optimized and then ported with programming frameworks portable across different GPU architectures, obtaining a further $\sim$$2$x acceleration factor. One critical section of the code consists in the computation of covariances, whose total number is $N_{\rm unk} \times (N_{\rm unk} - 1)/2$ and occupy $\sim$1 EB, being $N_{\rm unk}$$\sim$$5 \times 10^8$ the total number of unknowns. This "Big Data" issue cannot be faced with standard approaches: we defined an I/O-based pipeline made of two concurrently launched jobs, where one job, i.e., the LSQR, writes the files and the second job reads them, iteratively computes the covariances and deletes them. The pipeline does not present significant bottlenecks until a number of covariances elements equal to $\sim$$8 \times 10^6$. The code currently runs in production on Leonardo CINECA infrastructure.

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

The MPI + CUDA Gaia AVU-GSR Parallel Solver Toward Next-generation Exascale Infrastructures

We ported to the GPU with CUDA the Astrometric Verification Unit-Global Sphere Reconstruction (AVU-GSR) Parallel Solver developed for the ESA Gaia mission, by optimizing a previous OpenACC porting of this application. The code aims to find, with a [10,100]$μ$as precision, the astrometric parameters of $\sim$$10^8$ stars, the attitude and instrumental settings of the Gaia satellite, and the global parameter $γ$ of the parametrized Post-Newtonian formalism, by solving a system of linear equations, $A\times x=b$, with the LSQR iterative algorithm. The coefficient matrix $A$ of the final Gaia dataset is large, with $\sim$$10^{11} \times 10^8$ elements, and sparse, reaching a size of $\sim$10-100 TB, typical for the Big Data analysis, which requires an efficient parallelization to obtain scientific results in reasonable timescales. The speedup of the CUDA code over the original AVU-GSR solver, parallelized on the CPU with MPI+OpenMP, increases with the system size and the number of resources, reaching a maximum of $\sim$14x, >9x over the OpenACC application. This result is obtained by comparing the two codes on the CINECA cluster Marconi100, with 4 V100 GPUs per node. After verifying the agreement between the solutions of a set of systems with different sizes computed with the CUDA and the OpenMP codes and that the solutions showed the required precision, the CUDA code was put in production on Marconi100, essential for an optimal AVU-GSR pipeline and the successive Gaia Data Releases. This analysis represents a first step to understand the (pre-)Exascale behavior of a class of applications that follow the same structure of this code. In the next months, we plan to run this code on the pre-Exascale platform Leonardo of CINECA, with 4 next-generation A200 GPUs per node, toward a porting on this infrastructure, where we expect to obtain even higher performances.

astro-ph.IM↗
arXiv2022

The Gaia AVU-GSR parallel solver: preliminary studies of a LSQR-based application in perspective of exascale systems

The Gaia Astrometric Verification Unit-Global Sphere Reconstruction (AVU-GSR) Parallel Solver aims to find the astrometric parameters for $\sim$10$^8$ stars in the Milky Way, the attitude and the instrumental specifications of the Gaia satellite, and the global parameter $γ$ of the post Newtonian formalism. The code iteratively solves a system of linear equations, $\mathbf{A} \times \vec{x} = \vec{b}$, where the coefficient matrix $\mathbf{A}$ is large ($\sim$$10^{11} \times 10^8$ elements) and sparse. To solve this system of equations, the code exploits a hybrid implementation of the iterative PC-LSQR algorithm, where the computation related to different horizontal portions of the coefficient matrix is assigned to separate MPI processes. In the original code, each matrix portion is further parallelized over the OpenMP threads. To further improve the code performance, we ported the application to the GPU, replacing the OpenMP parallelization language with OpenACC. In this port, $\sim$95% of the data is copied from the host to the device at the beginning of the entire cycle of iterations, making the code $compute$ $bound$ rather than $data$$-$$transfer$ $bound$. The OpenACC code presents a speedup of $\sim$1.5 over the OpenMP version but further optimizations are in progress to obtain higher gains. The code runs on multiple GPUs and it was tested on the CINECA supercomputer Marconi100, in anticipation of a port to the pre-exascale system Leonardo, that will be installed at CINECA in 2022.

astro-ph.IM↗
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

The Monitoring, Logging, and Alarm system for the Cherenkov Telescope Array

We present the current development of the Monitoring, Logging and Alarm subsystems in the framework of the Array Control and Data Acquisition System (ACADA) for the Cherenkov Telescope Array (CTA). The Monitoring System (MON) is the subsystem responsible for monitoring and logging the overall array (at each of the CTA sites) through the acquisition of monitoring and logging information from the array elements. The MON allows us to perform a systematic approach to fault detection and diagnosis supporting corrective and predictive maintenance to minimize the downtime of the system. We present a unified tool for monitoring data items from the telescopes and other devices deployed at the CTA array sites. Data are immediately available for the operator interface and quick-look quality checks and stored for later detailed inspection. The Array Alarm System (AAS) is the subsystem that provides the service that gathers, filters, exposes, and persists alarms raised by both the ACADA processes and the array elements supervised by the ACADA system. It collects alarms from the telescopes, the array calibration, the environmental monitoring instruments and the ACADA systems. The AAS sub-system also creates new alarms based on the analysis and correlation of the system software logs and the status of the system hardware providing the filter mechanisms for all the alarms. Data from the alarm system are then sent to the operator via the human-machine interface.

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↗
arXiv2018

The Global sphere reconstruction (GSR) - Demonstrating an independent implementation of the astrometric core solution for Gaia

Context. The Gaia ESA mission will estimate the astrometric and physical data of more than one billion objects, providing the largest and most precise catalog of absolute astrometry in the history of Astronomy. The core of this process, the so-called global sphere reconstruction, is represented by the reduction of a subset of these objects which will be used to define the celestial reference frame. As the Hipparcos mission showed, and as is inherent to all kinds of absolute measurements, possible errors in the data reduction can hardly be identified from the catalog, thus potentially introducing systematic errors in all derived work. Aims. Following up on the lessons learned from Hipparcos, our aim is thus to develop an independent sphere reconstruction method that contributes to guarantee the quality of the astrometric results without fully reproducing the main processing chain. Methods. Indeed, given the unfeasibility of a complete replica of the data reduction pipeline, an astrometric verification unit (AVU) was instituted by the Gaia Data Processing and Analysis Consortium (DPAC). One of its jobs is to implement and operate an independent global sphere reconstruction (GSR), parallel to the baseline one (AGIS, namely Astrometric Global Iterative Solution) but limited to the primary stars and for validation purposes, to compare the two results, and to report on any significant differences. Results. Tests performed on simulated data show that GSR is able to reproduce at the sub-$μ$as level the results of the AGIS demonstration run presented in Lindegren et al. (2012). Conclusions. Further development is ongoing to improve on the treatment of real data and on the software modules that compare the AGIS and GSR solutions to identify possible discrepancies above the tolerance level set by the accuracy of the Gaia catalog.

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↗
arXiv2013

Astrometric tests of General Relativity in the Solar System: mathematical and computational scenarios

We review the mathematical models available for relativistic astrometry, discussing the different approaches and their accuracies in the context of the modern experiments from space like Gaia and GAME, and we show how these models can be applied to the real world, and their consequences from the mathematical and numerical point of view, with specific reference to the case of Gaia, whose launch is due before the end of the year.

gr-qc↗