Decomposition and Classification of Spectral Lines in Astronomical Radio Data Cubes

  • Vincent Mazet
  • Christophe Collet
  • Bernd Vollmer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5575)


The natural output of imaging spectroscopy in astronomy is a 3D data cube with two spatial and one frequency axis. The spectrum of each image pixel consists of an emission line which is Doppler-shifted by gas motions along the line of sight. These data are essential to understand the gas distribution and kinematics of the astronomical object. We propose a two-step method to extract coherent kinematic structures from the data cube. First, the spectra are decomposed into a sum of Gaussians using a Bayesian method to obtain an estimation of spectral lines. Second, we aim at tracking the estimated lines to get an estimation of the structures in the cube. The performance of the approach is evaluated on a real radio-astronomical observation.


Bayesian inference MCMC spectrum decomposition multicomponent image spiral galaxy NGC 4254 


  1. 1.
    Cappé, O., Robert, C.P., Rydèn, T.: Reversible jump, birth-and-death and more general continuous time Markov chain Monte Carlo samplers. J. Roy. Stat. Soc. B 65, 679–700 (2003)CrossRefzbMATHMathSciNetGoogle Scholar
  2. 2.
    Cardoso, J.-F., Snoussi, H., Delabrouille, J., Patanchon, G.: Blind separation of noisy Gaussian stationary sources. Application to cosmic microwave background imaging. In: 11th EUSIPCO (2002)Google Scholar
  3. 3.
    Chellappa, R., Jain, A.: Markov random fields. Theory and application. Academic Press, London (1993)Google Scholar
  4. 4.
    Devroye, L.: Non-uniform random variate generation. Springer, Heidelberg (1986)CrossRefzbMATHGoogle Scholar
  5. 5.
    Flitti, F., Collet, C., Vollmer, B., Bonnarel, F.: Multiband segmentation of a spectroscopic line data cube: application to the HI data cube of the spiral galaxy NGC 4254. EURASIP J. Appl. Si. Pr. 15, 2546–2558 (2005)CrossRefzbMATHGoogle Scholar
  6. 6.
    Gelman, A., Roberts, G., Gilks, W.: Efficient Metropolis jumping rules. In: Bernardo, J., Berger, J., Dawid, A., Smith, A. (eds.) Bayesian Statistics 5, pp. 599–608. Oxford University Press, Oxford (1996)Google Scholar
  7. 7.
    Green, P.J.: Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82, 711–732 (1995)CrossRefzbMATHMathSciNetGoogle Scholar
  8. 8.
    Idier, J. (ed.): Bayesian approach to inverse problems. ISTE Ltd. and John Wiley & Sons Inc., Chichester (2008)zbMATHGoogle Scholar
  9. 9.
    Mazet, V., Brie, D., Idier, J.: Simulation of positive normal variables using several proposal distributions. In: 13th IEEE Workshop Statistical Signal Processing (2005)Google Scholar
  10. 10.
    Mazet, V.: Développement de méthodes de traitement de signaux spectroscopiques : estimation de la ligne de base et du spectre de raies. PhD. thesis, Nancy University, France (2005)Google Scholar
  11. 11.
    Phookun, B., Vogel, S.N., Mundy, L.G.: NGC 4254: a spiral galaxy with an m = 1 mode and infalling gas. Astrophys. J. 418, 113–122 (1993)CrossRefGoogle Scholar
  12. 12.
    Robert, C., Casella, G.: Monte Carlo statistical methods. Springer, Heidelberg (2002)zbMATHGoogle Scholar
  13. 13.
    Rousseeuw, P., Leroy, A.: Robust Regression and Outlier Detection. Series in Applied Probability and Statistics. Wiley-Interscience, Hoboken (1987)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Vincent Mazet
    • 1
  • Christophe Collet
    • 1
  • Bernd Vollmer
    • 2
  1. 1.LSIIT (UMR 7005 University of Strasbourg–CNRS)Illkirch CedexFrance
  2. 2.Observatoire Astronomique de Strasbourg (UMR 7550 University of Strasbourg–CNRS)StrasbourgFrance

Personalised recommendations