Observations of the mid-infrared (mid-IR, 3-15 μm) spectra of photo-dissociation regions reveal ubiquitous, broad and intense emission bands, the aromatic infrared bands (AIBs), attributed to polycyclic aromatic hydrocarbons (PAHs). Studies of the AIBs showed spectral variations (e.g. in the band positions) between different astrophysical objects, or even within single object, thanks to hyperspectral images. The James Webb Space Telescope (JWST) will allow to get further spectral and spatial details compared to former space observatories. This will come with large data sets, which will require specific tools in order to perform efficient scientific analysis.
We propose in this study a method based on blind signal separation to reduce the analysis of such large data set to that of a small number of elementary spectra, spectrally representative of the data set and physically interpretable as the spectra of populations of mid-IR emitters. The robustness and fastness of the method are improved compared to former algorithms. It is tested on a ISO-SWS data set, which approaches the best the characteristics of JWST data, from which four elementary spectra are extracted, attributed to cationic, neutral PAHs, evaporating very small grains and large and ionized PAHs.