Blind Signal Separation Methods for the Identification of Interstellar Carbonaceous Nanoparticles

TL;DR

This paper introduces a novel application of Blind Signal Separation techniques, specifically FastICA and NMF, to analyze interstellar dust spectra, revealing the spectra and spatial distribution of carbonaceous nanoparticles in space.

Contribution

It presents a new approach using ICA and NMF for astrophysical spectral analysis, enabling identification and spatial mapping of interstellar nanoparticles.

Findings

Unveiled source spectra of three types of interstellar carbonaceous nanoparticles.

Demonstrated the use of extracted spectra for spatial distribution analysis.

Provided a basis for interpreting infrared emission spectra of interstellar dust.

Abstract

The use of Blind Signal Separation methods (ICA and other approaches) for the analysis of astrophysical data remains quite unexplored. In this paper, we present a new approach for analyzing the infrared emission spectra of interstellar dust, obtained with NASA's Spitzer Space Telescope, using FastICA and Non-negative Matrix Factorization (NMF). Using these two methods, we were able to unveil the source spectra of three different types of carbonaceous nanoparticles present in interstellar space. These spectra can then constitute a basis for the interpretation of the mid-infrared emission spectra of interstellar dust in the Milky Way and nearby galaxies. We also show how to use these extracted spectra to derive the spatial distribution of these nanoparticles.