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Bayesian Multiscale Deconvolution Applied to Gamma-ray Spectroscopy

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Abstract

A common task in gamma-ray astronomy is to extract spectral information, such as model constraints and incident photon spectrum estimates, given the measured energy deposited in a detector and the detector response. This is the classic problem of spectral “deconvolution” or spectral inversion [2]. The methods of forward folding (i.e. parameter fitting) and maximum entropy “deconvolution” (i.e. estimating independent input photon rates for each individual energy bin) have been used successfully for gamma-ray solar flares (e.g. [5]). Nowak and Kolaczyk [4] have developed a fast, robust, technique using a Bayesian multiscale frame-work that addresses many problems with added algorithmic advantages. We briefly mention this new approach and demonstrate its use with time resolved solar flare gamma-ray spectroscopy.

Keywords

  • Light Curve
  • Model Constraint
  • Common Task
  • Deconvolution Technique
  • Deconvolved Spectrum

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Connors, A., Private Comm. (2000).

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  2. Craig, I. J. D., and Brown, J. C., Inverse Problems in Astronomy, Boston: Adam Hilger, Ltd., (1986).

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  3. Kolaczyk, E. D., in Bayesian Inference in Wavelet-based Models, edited by M. A. Vidkovic, New York City: Springer-Verlag, (1999).

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  4. Nowak, R. D., and Kolaczyk, E. D. in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, (1999).

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  5. G. Rank Ph.D. Thesis University of Munich (1997).

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© 2003 Springer-Verlag New York, Inc.

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Young, C.A. et al. (2003). Bayesian Multiscale Deconvolution Applied to Gamma-ray Spectroscopy. In: Statistical Challenges in Astronomy. Springer, New York, NY. https://doi.org/10.1007/0-387-21529-8_67

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  • DOI: https://doi.org/10.1007/0-387-21529-8_67

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-95546-9

  • Online ISBN: 978-0-387-21529-7

  • eBook Packages: Springer Book Archive