Issue |
A&A
Volume 690, October 2024
|
|
---|---|---|
Article Number | A203 | |
Number of page(s) | 17 | |
Section | Numerical methods and codes | |
DOI | https://doi.org/10.1051/0004-6361/202451096 | |
Published online | 08 October 2024 |
Sifting the debris: Patterns in the SNR population with unsupervised ML methods
1
INAF – Osservatorio Astrofisico di Catania,
Via Santa Sofia 78,
95123
Catania,
Italy
2
Department of Electrical, Electronic and Computer Engineering, University of Catania,
Viale Andrea Doria 6,
95125
Catania,
Italy
3
Universitá degli Studi di Milano-Bicocca,
Viale Sarca 336,
20126
Milano,
Italy
4
School of Mathematical and Physical Sciences,
12 Wally’s Walk, Macquarie University,
NSW 2109,
Australia
5
Institute of Space Sciences and Astronomy,
Maths & Physics Building, University of Malta,
Msida
MSD2080,
Malta
★ Corresponding author; filomena.bufano@inaf.it
Received:
13
June
2024
Accepted:
9
August
2024
Context. Supernova remnants (SNRs) carry vast amounts of mechanical and radiative energy that heavily influence the structural, dynamical, and chemical evolution of galaxies. To this day, more than 300 SNRs have been discovered in the Milky Way, exhibiting a wide variety of observational features. However, existing classification schemes are mainly based on their radio morphology.
Aims. In this work, we introduce a novel unsupervised deep learning pipeline to analyse a representative subsample of the Galactic SNR population (~50% of the total) with the aim of finding a connection between their multi-wavelength features and their physical properties.
Methods. The pipeline involves two stages: (1) a representation learning stage, consisting of a convolutional autoencoder that feeds on imagery from infrared and radio continuum surveys (WISE 22 μm, Hi-GAL 70 μm and SMGPS 30 cm) and produces a compact representation in a lower-dimensionality latent space; and (2) a clustering stage that seeks meaningful clusters in the latent space that can be linked to the physical properties of the SNRs and their surroundings.
Results. Our results suggest that this approach, when combined with an intermediate uniform manifold approximation and projection (UMAP) reprojection of the autoencoded embeddings into a more clusterable manifold, enables us to find reliable clusters. Despite a large number of sources being classified as outliers, most clusters relate to the presence of distinctive features, such as the distribution of infrared emission, the presence of radio shells and pulsar wind nebulae, and the existence of dust filaments.
Key words: surveys / ISM: supernova remnants / infrared: general / radio continuum: general
© The Authors 2024
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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