Conveners
Bandi a Cascata
- Ugo Becciani (Istituto Nazionale di Astrofisica (INAF))
In this update, we would like to report on the progress of the NeuroStarMap project, which aims to enable astronomers to obtain more reliable estimates of stellar and cosmological distances. Specifically, we will discuss the neural networks implemented as an initial approach to the project, their results, and the data preparation process that led to them.
CANDELA aims to develop a generalized methodology using machine learning and deep learning techniques to estimate the distances of stars and galaxies, leveraging distance indicators available in catalogs such as Gaia DR3 and the OGLE catalogs of variable stars, in combination with parameters extracted from photometric time series. At the same time, by making use of the rich collection of...
In the context of the SDEGnO project, we present recent advancements in the GPU optimization of a Monte Carlo code for spatial propagation.
By implementing modern C++ standard and CUDA libraries, and restructuring the code to evaluate multiple heliosphere parametrizations in parallel,
we achieved an extremely significant speed-up, greatly enhancing the performance of the simulation and its...
Analyzing spectral and spatial information across di.erent energy bands in supernova remnants is crucial for understanding their physical and chemical evolution. In this work, we proposed a novel deep learning methodology aimed at clustering FLUX and Equivalent Width (EW) maps corresponding to di.erent energy bands of individual supernovae. Our approach consists of three main phases: (1)...
The goal of the AstroClass project is to automate the extraction of some characteristic features of astrophysical structures through advanced machine learning techniques, with a focus on extracting density, pressure, and temperature profiles of galaxy clusters from multi-frequency observational data and brightness surface profiles. Leveraging Interpo.latory AutoEncoder neural networks, the...
