Abstract
One of the objectives of nuclear spectroscopy is to estimate the varying counting rate activity of unknown radioactive sources. When this activity is high, however, nonparalyzable detectors suffer from a type of distortion called pile-up effect, when pulses created from different sources tend to overlap. This distortion leads to an underestimation of the activity, which explains the interest of methods for individual pulse separation. We suggest in this paper a two-step method for a better counting rate estimation: the signal is first approximated using a block-sparse regression method, allowing to separate individual pulses quite well. We then estimate their arrival times and plug them into a known activity estimator. Results on simulations and real data illustrate the efficiency of the proposed approach.
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References
Beck, A., Teboulle, M.: A fast iterative Shrinkage-Thresholding algorithm for linear inverse problems. SIAM Journal on Imaging Sciences 2, 183 (2009)
Chen, S.S., Donoho, D.L., Saunders, M.A.: Atomic decomposition by basis pursuit. SIAM Journal on Scientific Computing 20, 33–61 (1998)
Efron, B., Hastie, T., Johnstone, I., Tibshirani, R.: Least angle regression. Annals of Statistics 32(2), 407–499 (2004)
Knoll, G.F.: Radiation Detection and Measurement, 2nd edn. Wiley (1989)
Lewis, P.A.W., Shedler, G.S.: Statistical analysis of non-stationary series of events in a data base system. IBM J. Res. Dev. 20(5), 465–482 (1976)
Meinshausen, N., Yu, B.: Lasso-type recovery of sparse representations for high-dimensional data. The Annals of Statistics 37(1), 246–270 (2009)
Michotte, C., Nonis, M.: Experimental comparison of different dead-time correction techniques in single-channel counting experiments. Nuclear Instruments and Methods in Physics Research Section A 608(1), 163–168 (2009)
Trigano, T., Sepulcre, Y., Roitman, M., Aferiat, U.: On nonhomogeneous activity estimation in gamma spectrometry using sparse signal representation. In: 2011 IEEE Statistical Signal Processing Workshop (SSP), pp. 649–652. IEEE (June 2011)
Wainwright, M.J.: Sharp thresholds for high-dimensional and noisy sparsity recovery using l1-constrained quadratic programming (Lasso). IEEE Trans. Inf. Theor. 55(5), 2183–2202 (2009)
Yuan, M., Lin, Y.: Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society, B 68(1), 49–67 (2006)
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© 2012 Springer-Verlag Berlin Heidelberg
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Trigano, T., Sepulcre, Y. (2012). Regularized Sparse Representation for Spectrometric Pulse Separation and Counting Rate Estimation. In: Theis, F., Cichocki, A., Yeredor, A., Zibulevsky, M. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2012. Lecture Notes in Computer Science, vol 7191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28551-6_24
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DOI: https://doi.org/10.1007/978-3-642-28551-6_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28550-9
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