Learning mid-IR emission spectra of polycyclic aromatic hydrocarbon populations from observations.

ABSTRACT: The James Webb Space Telescope (JWST) will deliver an unprecedented quantity of high-quality spectral data over the 0.6-28 μm range. It will combine sensitivity, spectral resolution, and spatial resolution. Specific tools are required to provide efficient scientific analysis of such large data sets. Our aim is to illustrate the potential of unsupervised learning methods to get insights into chemical variations in the populations that carry the aromatic infrared bands (AIBs), more specifically polycyclic aromatic hydrocarbon (PAH) species and carbonaceous very small grains (VSGs). We present a method based on linear fitting and blind signal separation for extracting representative spectra for a spectral data set. The method is fast and robust, which ensures its applicability to JWST spectra

SUBMITTER: Foschino S 

PROVIDER: S-EPMC7116302 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature