A short summary of the activities of the Astroinformatics team from Naples
The next generation of radio telescope arrays promises unparalleled sensitivity and resolution, unveiling a wealth of faint and diffuse radio sources in galaxy clusters and beyond. Conventional cataloging approaches struggle with the complexity of such data. To address this, we introduce Radio U-Net, a convolutional neural network based on the U-Net architecture, tailored for identifying...
The advent of next-generation radio telescopes, such as the Square Kilometre Array, promises to revolutionize radio astronomy by generating unprecedented data volumes that challenge traditional processing methods. Deep learning techniques have shown significant potential in tackling various radio analysis tasks, but their effectiveness is often hindered by the scarcity of large, balanced...
The Solar Physics group at the Astrophysical Observatory of Torino (OATo), part of the National Institute for Astrophysics (INAF), stands at the forefront of solar physics research. It specializes in investigating the solar wind and eruptive events - from the dynamic layers of the solar atmosphere to the complexities of the inner heliosphere - through a multi-instrument, multi-spacecraft...
This talk provides an overview of the work carried out by the MIDA (Methods for Image and Data Analysis) group at Dipartimento di Matematica, Università di Genova, focusing on the application of artificial intelligence to solar physics and space weather forecasting. Our team combines deep learning techniques with key physical principles to develop models that can predict solar activity and...
One of the major challenges in the context of the Cosmic Microwave Background (CMB) radiation is to detect a polarization pattern, the so called B-modes of CMB polarization, that are thought to be directly linked to the space-time fluctuations present in the Universe at the very first instants of life. To date, several challenges have prevented to detect the B-modes partly because of the lower...
This study evaluates the use of Quantum Convolutional Neural Networks (QCNNs) for identifying signals resembling Gamma-Ray Bursts (GRBs) within simulated astrophysical datasets in the form of light curves. The task addressed here focuses on distinguishing GRB-like signals from background noise in simulated Cherenkov Telescope Array Observatory (CTAO) data, the next-generation astrophysical...
The study of exoplanetary atmospheres traditionally relies on forward models to analytically compute the spectrum of an exoplanet by fine-tuning numerous chemical and physical parameters. However, the high-dimensionality of parameter space often results in a significant computational overhead. In this work, we introduce a novel approach to atmospheric retrieval leveraging on quantum extreme...
Metallicity is a key parameter in stellar evolution, yet its determination often relies on high-resolution spectra, limiting observational coverage. In this work, we explore the use of Transformer-based models to infer metallicity directly from photometric light curves, leveraging their sequential learning capabilities and attention to temporal patterns. Our results show performance comparable...
The Imaging X-ray Polarimetry Explorer (IXPE) has demonstrated that X-ray polarimetry is a powerful tool in astronomy, offering crucial insights into emission mechanisms and the geometry of compact objects. It operates within the 2–8 keV energy range. These measurements are made possible by three polarization-sensitive Gas Pixel Detectors (GPDs) at the core of the focal plane, each equipped...
Large astronomical surveys play a significant role in modern astrophysics, providing extensive datasets for various studies, i.e. stellar populations and galactic evolution. However, systematic discrepancies between spectroscopic surveys—arising from differences in instrumentation, spectral coverage, and analysis techniques—pose significant challenges for studies that rely on data from...
