Third Technical Meeting Spoke 3

Session

Bandi a Cascata

28 May 2025, 16:50

Perugia

Conveners

Bandi a Cascata

• There are no conveners in this block

Bandi a Cascata

• Ugo Becciani

8. CATMAD: Synthesis of nonthermal emission from 3-D MHD simulations of astrophysical plasmas

Fabrizio Bocchino (Istituto Nazionale di Astrofisica (INAF))

28/05/2025, 16:50

CATMAD is a project aimed at developing an advanced diagnostic tool for the analysis of nonthermal emission stemming from supernova remnants and astrophysical plasmas. 
Through post-processing of state-of-the-art 3-D MHD simulations of supernova remnants, we synthesise the multi-wavelength emission (from the radio band to gamma-rays) to get an accurate comparison with actual observations. We...

23. PAGUSCI - Porting of the CAMB code to GPU

Loriano Storchi (Chieti University)

28/05/2025, 17:05

In the presentation we will outline the essential stages required to profile and port the CAMB code onto a GPU architecture. The process encompasses detailed performance analysis of the existing CAMB code, identifying bottlenecks, and strategically adapting code segments for GPU execution. In this presentation, we will primarily focus on the code modifications necessary to adapt the CAMB code...

66. ASTRAI

Vincenzo Del Zoppo (KOEXAI)

28/05/2025, 17:20

We present ASTRAI, an end-to-end framework that accelerates Type II supernova (SN) research by integrating physically motivated models with generative diffusion techniques to produce a high-fidelity synthetic dataset for deep learning (DL)–based SN characterization. First, we aggregate and homogenize multi–survey photometric and spectroscopic observations from Carnegie, WISeREP, Zenodo, and...

65. SPECTRA & GRAIL

Alberto Garinei (UniMarconi)

29/05/2025, 09:15

The SPECTRA AI project addresses the challenge of identifying and classifying transient gamma-ray emissions in Fermi LAT observations using advanced artificial intelligence methods. Transient phenomena such as gamma-ray bursts and flares exhibit complex spatio-temporal dynamics, necessitating robust pattern-recognition frameworks.
The GRAIL project (Gamma-Ray Imaging with Deep Learning) aims...

68. GRAIS

Stefano Calì (KOEXAI)

29/05/2025, 09:30

The GRAIS project (Gamma-Ray Artificial Intelligence System) aims to develop innovative Deep Learning and Artificial Intelligence (AI) techniques to identify high-energy transient events, such as orphan gamma-ray burst (GRB) afterglows, in data collected by the Fermi Gamma-ray Large Area Space Telescope (GLAST). By leveraging state-of-the-art anomaly detection algorithms tailored to our...

57. AI-Legs Status Report

Francesco Conventi (INFN)

29/05/2025, 09:45

Gamma-ray astronomy in the MeV energy range offers the opportunity to investigate important scientific topics, such as nuclear processes, compact objects, cosmic rays and gamma-ray bursts (GRBs). However, in this energy range there is a significant lack of sensitivity compared to other gamma-ray bands. Imaging techniques represent a fundamental tool for signal reconstruction and analysis in...

4. Astrodata Status Report

Davide Giannuzzi (Alkemy)

29/05/2025, 10:00

In this update, we would like to provide you with an overview of the list of features that will make up the Astrodata tool, which we have now outlined with precision and detail. The various features will enable the creation of a framework that can be used to robustly apply machine learning algorithms within the workflow of astrophysical research. We will also report on the development progress...

5. AstroTool: Rearchitecting VisIVO through HPC and Remote Visualization

Marco Di Francesco (Alkemy)

29/05/2025, 10:15

AstroTool aims to evolve VisIVO into a next-generation client/server platform for the exploration and visualization of large FITS files from SKA and similar facilities. Building on the legacy of the ViaLactea Visual Analytics tool, AstroTool leverages modern High Performance Computing (HPC) infrastructures and a distributed, service-oriented cluster architecture to address the limitations of...

59. Machine Learning and Hardware Acceleration for Space-Based Detection: Particle Track Reconstruction and Low-Energy Gamma Imaging

Andrea Abba (Nuclear Instruments)

29/05/2025, 10:30

The Spartan and LEGIMaC projects share a common goal: advancing space-based detection technologies by integrating cutting-edge machine learning algorithms with energy-efficient hardware platforms, such as FPGAs and GPUs. Both projects address critical challenges in the reconstruction and classification of complex physical events in high-rate, noisy environments—typical of astrophysics...

67. DeepCosmoNet

Vincenzo Del Zoppo (KOEXAI)

29/05/2025, 10:45

Understanding the large-scale structure of the universe, known as the Cosmic Web, relies heavily on the analysis of numerical 3D N-body simulations.
Analyzing the output of those simulations remains computationally intensive due to large 3D point clouds and complex clustering tasks.
Traditional methods—such as FoF, SUBFIND, and ZOBOV often operate as separate, CPU-bound tools with limited...

49. Data Quality, ATS & Fraud Detection Status Report

Dr Riccardo Crupi (Istituto Nazionale di Astrofisica (INAF))

70. Data Quality, ATS & Fraud DetectionWe will present recent advancements in our work on Data Quality, ATS, and Fraud Detection, developed in collaboration with Banca Intesa Sanpaolo. Specifically, we describe two complementary approaches. The first builds on our previous work combining Feature Engineering, Self-Organizing Maps (SOM), and Isolation Forest (IF). In this case, we focus on analyzing the behavior of specific instances across different time windows. The second approach investigates the use of autoencoders to identify potential outliers. We will also briefly illustrate how the same methodology (Feature Engineering, SOM, and IF) can be applied to the Astrophysics domain, where it enables the identification of a relatively pure sample of Active Galactic Nuclei (AGN) among galaxies and stars. Moreover, we introduce our fraud detection strategy, which uses Random Forest to identify potentially fraudulent events, and applies the K-Nearest Neighbors (KNN) algorithm at a user-level to identify anomalous operations by comparing each transaction to the user's typical behavior. Finally, given the good results obtain in the high energy astrophysics, it is also presented briefly FoCUS, an evolution of cusum, and a possible application on the Banking data.

Ylenia Maruccia (Istituto Nazionale di Astrofisica (INAF))

47. HaMMon: Automated Photogrammetric Workflow for Environmental Digital Twin Generation and Hazard Assessment

Leonardo Pelonero (Istituto Nazionale di Astrofisica (INAF))

This work reports recent advancements in the development of an automated photogrammetric pipeline for generating 3D geospatial digital twins, Data Visualization and Artificial Intelligence technologies, aimed at environmental monitoring and hazard assessment.
For interactive 3D visualization and analysis, the pipeline integrates dense 3D reconstructions into the CesiumJS web environment and...

48. HaMMon: Segmentation and Classification of aerial images

Nicoletta Sanvitale (Istituto Nazionale di Astrofisica (INAF) - Istituto di Radioastronomia)

52. IDL: INAF Contribution

Giacomo Coran (Istituto Nazionale di Astrofisica (INAF))

50. Interoperable Data Lake (IDL)

53. Serial Code Porting on HPC & Quantum Computing

Manuele Innocenti (SOGEI)

54. Status of IGUC project

Deborah Busonero (Istituto Nazionale di Astrofisica (INAF))

51. The HaMMon Project