Spectral similarities in galaxies through an unsupervised classification of spaxels
Hugo Chambon (IPAG), Didier Fraix-Burnet (IPAG)

TL;DR
This study introduces an unsupervised method for classifying galaxy spaxels using spectral data, revealing detailed structures and new features in galaxy images from multiple telescopes.
Contribution
It presents the first unsupervised classification approach for galaxy spaxels that leverages spectral and spatial data, identifying known and novel astrophysical structures.
Findings
Identified known HII regions and gradients in JKB 18.
Mapped AGN and star-forming regions in NGC 1068.
Discovered a new ionised structure in NGC 4151.
Abstract
We present the first unsupervised classification of spaxels in hyperspectral images of individual galaxies. Classes identify regions by spectral similarity and thus take all the information into account that is contained in the data cubes (spatial and spectral).We used Gaussian mixture models in a latent discriminant subspace to find clusters of spaxels. The spectra were corrected for small-scale motions within the galaxy based on emission lines with an automatic algorithm. Our data consist of two MUSE/VLT data cubes of JKB 18 and NGC 1068 and one NIRSpec/JWST data cube of NGC 4151.Our classes identify many regions that are most often easily interpreted. Most of the 11 classes that we find for JKB 18 are identified as photoionised by stars. Some of them are known HII regions, but we mapped them as extended, with gradients of ionisation intensities. One compact structure has not been…
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Taxonomy
TopicsSpectroscopy and Chemometric Analyses
