Deep learning in the SKA era: patterns in the SNR population with unsupervised ML methods

F. Bufano; C. Bordiu; T. Cecconello; M. Munari; A. M. Hopkins; A. Ingallinera; P. Leto; S. Loru; S. Riggi; E. Sciacca; G. Vizzari; A. Demarco; C. S. Buemi; F. Cavallaro; C. Trigilio; G. Umana

doi:10.1117/12.3026706

25 July 2024 Deep learning in the SKA era: patterns in the SNR population with unsupervised ML methods

F. Bufano, C. Bordiu, T. Cecconello, M. Munari, A. M. Hopkins, A. Ingallinera, P. Leto, S. Loru, S. Riggi, E. Sciacca, G. Vizzari, A. Demarco, C. S. Buemi, F. Cavallaro, C. Trigilio, G. Umana

Author Affiliations +

Proceedings Volume 13101, Software and Cyberinfrastructure for Astronomy VIII; 131014N (2024) https://doi.org/10.1117/12.3026706
Event: SPIE Astronomical Telescopes + Instrumentation, 2024, Yokohama, Japan

Conference Poster

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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F. Bufano, C. Bordiu, T. Cecconello, M. Munari, A. M. Hopkins, A. Ingallinera, P. Leto, S. Loru, S. Riggi, E. Sciacca, G. Vizzari, A. Demarco, C. S. Buemi, F. Cavallaro, C. Trigilio, and G. Umana "Deep learning in the SKA era: patterns in the SNR population with unsupervised ML methods", Proc. SPIE 13101, Software and Cyberinfrastructure for Astronomy VIII, 131014N (25 July 2024); https://doi.org/10.1117/12.3026706

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