Conveners
WP3
- Fabio Gargano (INFN Bari)
- Fabio Roberto Vitello
WP3
- Fabio Roberto Vitello
- Fabio Gargano (INFN Bari)
WP3
- There are no conveners in this block
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Paolo Campeti (INFN Sezione di Ferrara, ICSC)28/05/2025, 09:00
We introduce a novel, fast, and efficient generative model built upon scattering covariances, the most recent iteration of the scattering transforms statistics. This model is designed to augment by several orders of magnitude the number of map simulations in datasets
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of computationally expensive Cosmic Microwave Background (CMB) instrumental systematics simulations, including their non-Gaussian... -
Chiara Stuardi (Istituto Nazionale di Astrofisica (INAF))28/05/2025, 09:15
In this talk, I will present the latest developments in the Radio U-Net pipeline. Radio U-Net is a convolutional neural network derived from the U-Net architecture, specifically designed to perform rapid and automated segmentation of diffuse radio emission in extensive low-frequency surveys. Its application to the LOFAR Two Metre Sky Survey (LoTSS) has demonstrated high accuracy: when the...
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James Davies (Scuola Normale Superiore)28/05/2025, 09:30
Understanding the epochs of cosmic dawn and reionisation requires us to leverage multi-wavelength and multi-tracer observations, with each dataset providing a complimentary piece of the puzzle. To interpret such data, we update the public simulation code, 21cmFASTv4, to include a discrete source model based on stochastic sampling of conditional mass functions and semi-empirical galaxy...
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Yilong Zhang (Scuola Normale Superiore (Pisa))28/05/2025, 09:45
The distribution of neutral hydrogen mapped by SKA-Low through the 21 cm signal provides a unique and powerful probe of the cosmic reionization process. However, this method faces significant challenges due to severe foreground contamination, which can exceed the cosmological signal by up to five orders of magnitude.
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Cross-correlating 21 cm and line intensity maps such as [CII] and CO offers... -
Andrea Adelfio (INFN Perugia)28/05/2025, 10:00
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Eleonora Villa (Istituto Nazionale di Astrofisica (INAF))28/05/2025, 10:15
Complex inference tasks such as those in Pulsar Timing Array (PTA) data analysis rely on Bayesian frameworks. The high-dimensional parameters space and the strong interdependencies among astrophysical, pulsar noise and nuisance parameters introduce significant challenges. We address two of them. The first focuses on speeding up the existing code for Bayesian inference by using NessAI, a nested...
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Federica Cuna (INFN-BARI)28/05/2025, 10:30
The integration of advanced Artificial Intelligence (AI) techniques into astroparticle experiments represents a transformative step for data analysis and experimental design. With the increasing complexity of space missions, the adoption of AI technologies becomes crucial for optimizing performance and achieving robust scientific outcomes.
This study focuses on the development of innovative...
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Maria Bossa (INFN)28/05/2025, 10:45
We present a machine learning approach for discriminating electromagnetic and hadronic showers in simulated data from a high-granularity LYSO space-based calorimeter. Two feature sets were explored: Vision Transformers were trained on the spatial coordinates and deposited energy of activated pixels, while XGBoost classifiers used reconstructed variables such as lateral and transverse moments...
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Giovanni Cavallotto (INFN Milano Bicocca)28/05/2025, 11:30
Simulating the effect of the Cosmic Rays (CR) on the signal of the High Frequency Telescope (HFT) of microwave detector experiment from space implies a computational expensive chain of Monte Carlo (MC) simulations. It started with the CR spectra at L2 mission location, then we propagated them into the detector materials, extract the hits deposited energies on the sensible area, propagate the...
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Karina Baeza Villagra (Istituto Nazionale di Astrofisica (INAF))28/05/2025, 11:45
The main aim of this project is to use RR Lyrae (RRL) variables to investigate the Milky Way’s early formation and evolution. We will provide the largest spectroscopic dataset for RRLs, using a mix of proprietary and publicly available spectroscopic catalogs. We provide accurate measurements of radial velocities and metallicities via the Delta-S method by using single epoch spectra. Moreover,...
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Irene De Blasi (UNITO)28/05/2025, 12:00
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Stefano Pio Cosentino (Istituto Nazionale di Astrofisica (INAF))28/05/2025, 12:45
The growing number of observed supernovae in current and upcoming surveys demands scalable and automated methods for their physical characterization. In this framework, we developed an AI-based pipeline to reconstruct bolometric light curves from multi-band photometric observations and infer physical parameters such as ejecta mass, explosion energy, and Ni-56 yield. Real data observation from...
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Piero Trevisan (INAF)
High-resolution cosmological simulations remain computationally expensive, limiting our ability to explore large volumes and complex physics like massive neutrinos and dynamic dark energy. Recent developments in AI-assisted super-resolution, particularly the work of Li et al. (2021), Ni et al. (2022), and Zhang et al. (2023) has shown that generative models can reconstruct high-resolution...
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Piero Trevisan (INAF)
We are currently working on super‑resolution of cosmological simulations by training a 2× StyleGAN neural network. After successful tests with a pretrained model on 64³‑particle simulations, we moved to CINECA G100 cluster to investigate training strategies for much larger simulations (512³→1024³ particles).
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Stefano Pio Cosentino (Istituto Nazionale di Astrofisica (INAF))
The growing number of observed supernovae in current and upcoming surveys demands scalable and automated methods for their physical characterization. In this framework, we developed an AI-based pipeline to reconstruct bolometric light curves from multi-band photometric observations and infer physical parameters such as ejecta mass, explosion energy, and Ni-56 yield. Real data observation from...
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Giuseppe Tudisco (Istituto Nazionale di Astrofisica (INAF))
We present the latest developments of VisIVO Visual Analytics, an interactive visualization tool that is evolving to meet the needs of the SKA and similar large-scale astrophysical projects. Originally designed as a desktop application, VisIVO is transitioning to a client-server architecture, allowing it to efficiently run remote visualization pipelines on remote servers. This shift also...
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Thomas Cecconello (Istituto Nazionale di Astrofisica (INAF))