Conveners
Bandi a Cascata
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Bandi a Cascata
- Ugo Becciani
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...
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...
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...
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...
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...
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...
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...
