F. Bufano, C. Bordiu, T. Cecconello, M. Munari, A. Hopkins, A. Ingallinera, P. Leto, S. Loru, S. Riggi, E. Sciacca, G. Vizzari, A. Demarco, C. Buemi, F. Cavallaro, C. Trigilio, G. Umana
The Square Kilometre Array precursors are starting to release the first data of their large-field continuum surveys, making clear that also in the field of radio astronomy, deep learning turns as the primary solution for handling an overwhelming volume of data. Within this framework, our research group is taking a forefront position in various research initiatives aimed at assessing the effectiveness of ML techniques on survey data from ASKAP and MeerKAT. In this work we show how an unsupervised multi-stage pipeline is able to discover physically meaningful clusters within the heterogeneous Supernova Remnant (SNR) population: a convolutional autoencoder extracts features from multiwavelength imagery of a SNR sample; then an unsupervised clustering process operates on the latent space. Despite a large number of outliers, we were able to find a new classification system, in which most clusters relate to the presence of certain features regarding not only the morphology but also the relative weight of the different frequencies.
The European Open Science Cloud (EOSC) aims to create a federated environment for hosting and processing research data, supporting science in all disciplines without geographical boundaries, so that data, software, methods and publications can be shared seamlessly as part of an Open Science community. This work presents the ongoing activities related to the implementation and integration into EOSC of Visual Analytics services for astrophysics, specifically addressing challenges related to data management, mapping and structure detection. These services provide visualisation capabilities to manage the data life cycle processes under FAIR principles, integrating data processing for imaging and multidimensional map creation and mosaicking and data analysis supported with machine learning techniques, for detection of structures in large scale multidimensional maps.
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