Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloSource separation in astrophysical maps using independent factor analysis
Anno di pubblicazione2003
Autore/iKuruoglu E.E.; Bedini L.; Paratore M.T.; Salerno E.; Tonazzini A.
Affiliazioni autoriCNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy;
Autori CNR e affiliazioni
  • inglese
AbstractA microwave sky map results from a combination of signals from various astrophysical sources, such as cosmic microwave background radiation, synchrotron radiation and galactic dust radiation. To derive information about these sources, one needs to separate them from the measured maps on different frequency channels. Our insufficient knowledge of the weights to be given to the individual signals at different frequencies makes this a difficult task. Recent work on the problem led to only limited success due to ignoring the noise and to the lack of a suitable statistical model for the sources. In this paper, we derive the statistical distribution of some source realizations, and check the appropriateness of a Gaussian mixture model for them. A source separation technique, namely, independent factor analysis, has been suggested recently in the literature for Gaussian mixture sources in the presence of noise. This technique employs a three layered neural network architecture which allows a simple, hierarchical treatment of the problem. We modify the algorithm proposed in the literature to accommodate for space-varying noise and test its performance on simulated astrophysical maps. We also compare the performances of an expectation-maximization and a simulated annealing learning algorithm in estimating the mixture matrix and the source model parameters. The problem with expectation-maximization is that it does not ensure global optimization, and thus the choice of the starting point is a critical task. Indeed, we did not succeed to reach good solutions for random initializations of the algorithm. Conversely, our experiments with simulated annealing yielded initialization-independent results. The mixing matrix and the means and coefficients in the source model were estimated with a good accuracy while some of the variances of the components in the mixture model were not estimated satisfactorily.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da479
Pagine a491
Pagine totali-
RivistaNeural networks
Attiva dal 1988
Editore: Pergamon, - New York
Paese di pubblicazione: Stati Uniti d'America
Lingua: inglese
ISSN: 0893-6080
Titolo chiave: Neural networks
Titolo proprio: Neural networks
Titolo abbreviato: Neural netw.
Numero volume della rivista16
Fascicolo della rivista-
Verificato da referee-
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:00182354300017)
  • Scopus (Codice:2-s2.0-0037379718)
Parole chiaveAstrophysical image processing, Independent factor analysis, Independent component analysis, Blind source separation, Cosmic microwave background radiation
Link (URL, URI)-
Titolo parallelo-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • ISTI — Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei-
Source separation in astrophysical maps using independent factor analysis (documento privato )
Descrizione: Codice Puma: cnr.isti/2003-A0-04
Tipo documento: application/pdf

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Area disciplinareComputer Science & Engineering
Area valutazione CIVRScienze e tecnologie per una società dell'informazione e della comunicazione