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Large spatial-spectral surveys are more and more common in astronomy. Thiscalls for the need of new methods to analyze such mega- to giga-pixeldata-cubes. In this paper we present a method to decompose such observationsinto a limited and comprehensive set of components. The original data can thenbe interpreted in terms of linear combinations of these components. The methoduses non-negative matrix factorization (NMF) to extract latent spectralend-members in the data. The number of needed end-members is estimated based onthe level of noise in the data. A Monte-Carlo scheme is adopted to estimate theoptimal end-members, and their standard deviations. Finally, the maps of linearcoefficients are reconstructed using non-negative least squares. We apply thismethod to a set of hyperspectral data of the NGC 7023 nebula, obtained recentlywith the HIFI instrument onboard the Herschel space observatory, and provide afirst interpretation of the results in terms of 3-dimensional dynamicalstructure of the region.

Blind decomposition of Herschel-HIFI spectral maps of the NGC 7023 nebula