Abstract
This paper summarises our research on the separation of astrophysical source maps from multichannel observations, utilising techniques ranging from fully blind source separation to Bayesian estimation. Each observed map is a mix of various source processes. Separating the individual sources from a set of observed maps is of great importance to astrophysicists. We first tested classical fully blind methods and then developed our approach by adopting generic source models and prior information about the mixing operator. We also exploited a Bayesian formulation to incorporate further prior information into the problem. Our test data sets simulate the ones expected by the forthcoming ESA’s mission Planck Surveyor Satellite mission.
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References
Attias, H: Independent factor analysis. Neural Computation 11, 803 (1999)
Baccigalupi, C., et al.: Neural networks and the separation of cosmic microwave background and astrophysical signals in sky maps, M.N. Royal Astronomical Society 318, 769 (2000)
Bedini, L., et al.: A semi-blind second-order approach for statistical source separation in astrophysical maps, ISTI-CNR, Pisa, Technical Report ISTI-2003-TR-35 (2003)
Costagli, M., Kuruoğlu, E.E., Ahmed, A.: Bayesian separation of independent components in astrophysical images using particle filters, ISTI-CNR, Pisa, Technical Report, ISTI-2003-TR-54 (2003)
Hyvärinen, A., Oja, E.: A fast fixed-point algorithm for independent component analysis. Neural Computation 9, 1483 (1997)
Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley, New York (2001)
Kuruoğlu, E.E., et al.: Source separation in astrophysical maps using independent factor analysis. Neural Networks 16, 479 (2003)
Kuruoğlu, E.E., Milani Comparetti, P.: Bayesian source separation of astrophysical images using Markov Chain Monte Carlo. In: Proc. PHYSTAT, Stanford, September 8-11 (2003)
Kuruoğlu, E.E., Tonazzini, A., Bianchi, L.: Source separation in astrophysical images modelled by Markov random fields. In: Submitted to ICIP 2004 (2004)
Maino, D., et al.: All-sky astrophysical component separation with fast independent component analysis (FastICA), M.N. Royal Astronomical Society 334, 53 (2002)
Tonazzini, A., et al.: Blind separation of auto-correlated images from noisy images using MRF models. In: Fourth International Symposium on Independent Component Analysis and Blind Source Separation, Nara, Japan, vol. 675 (2003)
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© 2004 Springer-Verlag Berlin Heidelberg
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Salerno, E., Tonazzini, A., Kuruoğlu, E.E., Bedini, L., Herranz, D., Baccigalupi, C. (2004). Source Separation Techniques Applied to Astrophysical Maps. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30134-9_57
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DOI: https://doi.org/10.1007/978-3-540-30134-9_57
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