High Performance Computing based simulations are crucial in Astrophysics and Cosmology, helping scientists investigate and understand complex astrophysical phenomena. Taking advantage of Exascale computing capabilities is essential for these efforts. However, the unprecedented architectural complexity of exascale systems impacts simulation codes. The SPACE Center of Excellence aims to re-engineer key astrophysical codes to adapt to these new computational challenges by adopting innovative programming paradigms and software solutions. Through co-design activities, SPACE brings together scientists, code developers, HPC experts, hardware manufacturers, and software developers. This collaboration enhances exascale astrophysics and cosmology applications, promoting the use of exascale and post-exascale computing capabilities. Additionally, SPACE addresses high-performance data analysis for the massive data outputs from exascale simulations, using machine learning and visualization tools. The project facilitates application deployment across platforms by focusing on code repositories and data sharing, integrating European astrophysical communities around exascale computing with standardized software and data protocols. In this paper, we present the SPACE Center of Excellence and the preliminary results achieved by the project.
F. Bufano, C. Bordiu, T. Cecconello, M. Munari, A. Hopkins, A. Ingallinera, P. Leto, S. Loru, S. Riggi, E. Sciacca, G. Vizzari, A. Demarco, C. Buemi, F. Cavallaro, C. Trigilio, G. Umana
The Square Kilometre Array precursors are starting to release the first data of their large-field continuum surveys, making clear that also in the field of radio astronomy, deep learning turns as the primary solution for handling an overwhelming volume of data. Within this framework, our research group is taking a forefront position in various research initiatives aimed at assessing the effectiveness of ML techniques on survey data from ASKAP and MeerKAT. In this work we show how an unsupervised multi-stage pipeline is able to discover physically meaningful clusters within the heterogeneous Supernova Remnant (SNR) population: a convolutional autoencoder extracts features from multiwavelength imagery of a SNR sample; then an unsupervised clustering process operates on the latent space. Despite a large number of outliers, we were able to find a new classification system, in which most clusters relate to the presence of certain features regarding not only the morphology but also the relative weight of the different frequencies.
KEYWORDS: Software development, Telescopes, Data modeling, Computer architecture, Control systems, Atmospheric Cherenkov telescopes, Data acquisition, Data archive systems, Design, Cameras
The Astrophysics with Italian Replicating Technology Mirrors (ASTRI) Mini-Array is an international collaboration led by the Italian National Institute for Astrophysics (INAF) and devoted to imaging atmospheric Cherenkov light for very-high γ-ray astrophysics, detection of cosmic-rays, and stellar Hambury-Brown intensity interferometry. The project is deploying an array of nine dual-mirror aplanatic imaging atmospheric Cherenkov telescopes of 4-m class at the Teide Observatory on Tenerife in the Canary Islands. Based on SiPM sensors, the focal plane camera covers an unprecedented field of view of 10.5 deg in diameter. The array is most sensitive to γ-ray radiation above 1 up to 200 TeV, with an angular resolution of 3 arcmin, better than the current particle arrays, such as LHAASO and HAWC. We describe the overall software architecture of the ASTRI Mini-Array and the software engineering approach for its development. The software covers the entire life cycle of the Mini-Array, from scheduling to remote operations, data acquisition, and processing until data dissemination. The on-site control software allows remote array operations from different locations, including automated reactions to critical conditions. All data are collected every night, and the array trigger is managed post facto. The high-speed networking connection between the observatory site and the Data Center in Rome allows for ready data availability for stereoscopic event reconstruction, data processing, and almost real-time science products generation.
KEYWORDS: Data modeling, Atmospheric Cherenkov telescopes, Control systems, Software development, Telescopes, Data processing, Data archive systems, Data acquisition, Calibration, Computer architecture
The ASTRI Mini-Array is an international collaboration led by the Italian National Institute for Astrophysics (INAF) and devoted to the imaging of atmospheric Cherenkov light for very-high gamma-ray astronomy. The project is deploying an array of 9 telescopes sensitive above 1 TeV. In this contribution, we present the architecture of the software that covers the entire life cycle of the observatory, from scheduling to remote operations and data dissemination. The high-speed networking connection available between the observatory site, at the Canary Islands, and the Data Center in Rome allows for ready data availability for stereo triggering and data processing.
The European Open Science Cloud (EOSC) aims to create a federated environment for hosting and processing research data, supporting science in all disciplines without geographical boundaries, so that data, software, methods and publications can be shared seamlessly as part of an Open Science community. This work presents the ongoing activities related to the implementation and integration into EOSC of Visual Analytics services for astrophysics, specifically addressing challenges related to data management, mapping and structure detection. These services provide visualisation capabilities to manage the data life cycle processes under FAIR principles, integrating data processing for imaging and multidimensional map creation and mosaicking and data analysis supported with machine learning techniques, for detection of structures in large scale multidimensional maps.
Large volumes of monitoring and logging data result from the operation of a large scale astrophysical observatory. In the last few years several “Big Data” technologies have been developed to deal with such volumes of data especially in the Internet of Things (IoT) framework. We present the logging, monitoring and alarm system architecture for the ASTRI Mini-Array aimed at supporting the analysis of scientific data and improving the operational activities of the telescope facility . A prototype was designed and built considering the latest software tools and concepts coming from Big Data and IoT and a particular relevance has been given in satisfying quality requirements such as performance, scalability and availability.
KEYWORDS: Databases, Data storage, Surface conduction electron emitter displays, Observatories, Data modeling, Galactic astronomy, Human-machine interfaces, Stars, 3D modeling, Interfaces
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.
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