Name
Title | ||
|---|---|---|
Blind signal separation methods for the identification of interstellar carbonaceous nanoparticles |
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-74494-8_85 | ICA |
Keywords | Field | DocType |
spitzer space telescope,carbonaceous nanoparticles,astrophysical data,blind signal separation method,non-negative matrix factorization,mid-infrared emission spectrum,infrared emission spectrum,different type,interstellar dust,interstellar carbonaceous nanoparticles,interstellar space,milky way,non negative matrix factorization,blind signal separation | Astrophysics,Emission spectrum,Cosmic dust,Independent component analysis,FastICA,Spitzer Space Telescope,Galaxy,Milky Way,Blind signal separation,Physics | Conference |
Volume | ISSN | ISBN |
4666 | Lecture Notes in Computer Science, 7th International Conference,
ICA 2007, London, UK, September 9-12, 2007 | 3-540-74493-2 |
Citations | PageRank | References |
1 | 0.37 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| O. Berné | 1 | 7 | 1.56 |
| Yannick Deville | 2 | 507 | 75.06 |
| C. Joblin | 3 | 7 | 1.23 |