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Journal of Radioanalytical and Nuclear Chemistry

, Volume 318, Issue 1, pp 117–124 | Cite as

Use of neural networks to analyze pulse shape data in low-background detectors

  • E. K. Mace
  • J. D. Ward
  • C. E. Aalseth
Article
  • 104 Downloads

Abstract

Pacific Northwest National Laboratory has accumulated years of data with ultra-low-background proportional counters collected in an on-site shallow underground laboratory. This large dataset of events is exploited to study the impact of using neural networks for data analysis compared to simple pulse shape discrimination (PSD). The PSD method can introduce false positives for overlapping event distributions; however, a neural network can separate and correctly classify these events. This paper describes the training, testing, and validation of a neural network, analysis of challenge datasets, and a comparison between the standard PSD approach and a dense, fully-connected neural network.

Keywords

Pulse shape discrimination Neural network Classification Low-background Gas proportional counter Machine learning 

Notes

Acknowledgements

We would like to thank Dr. Nathan Hodas and Dr. Court Corley with the Data Science group at PNNL for providing advice and support for this work. The research described in this paper is part of the Agile Deep Science Initiative at Pacific Northwest National Laboratory and was conducted under the Laboratory Directed Research and Development (LDRD) Program. This work was performed by Pacific Northwest National Laboratory under award number DE-AC05-76RL01830. The research presented in this paper utilized the PNNL Institutional Computing (PIC) resources at Pacific Northwest National Laboratory. Information Release Number: PNNL-SA-133676.

References

  1. 1.
    Aalseth CE, Day AR, Hoppe EW et al (2009) Design and construction of a low-background, internal-source proportional counter. J Radioanal Nucl Chem 282(1):233–237.  https://doi.org/10.1007/s10967-009-0258-5 CrossRefGoogle Scholar
  2. 2.
    Seifert A, Aalseth CE, Day AR, Fuller ES, Hoppe EW, Keillor ME, Mace EK, Overman CT, Warren GA (2013) The design, construction, and initial characterization of an ultra-low-background gas-proportional counting system. J Radioanal Nucl Chem 296(2):915–921.  https://doi.org/10.1007/s10967-012-2059-5 CrossRefGoogle Scholar
  3. 3.
    Aalseth CE, Bonicalzi RM, Cantaloub MG et al (2012) A shallow underground laboratory for low-background radiation measurements and materials development. Rev Sci Instrum 83(11):113503–113510.  https://doi.org/10.1063/1.4761923 CrossRefPubMedGoogle Scholar
  4. 4.
    Aalseth CE, Day AR, Haas DA et al (2011) Measurement of 37Ar to support technology for on-site inspection under the comprehensive nuclear-test-bantreaty. Nucl Instrum Meth A 652(1):58–61.  https://doi.org/10.1016/j.nima.2010.09.135 CrossRefGoogle Scholar
  5. 5.
    Aalseth CE, Day AR, Fuller ES et al (2013) A new shallow underground gas-proportional counting lab—first results and Ar-37 sensitivity. Appl Rad Isot 81:151–155.  https://doi.org/10.1016/j.apradiso.2013.03.050 CrossRefGoogle Scholar
  6. 6.
    Mace E, Aalseth C, Brandenberger J et al (2017) Methods for using argon-39 to age-date groundwater using ultra-low-background proportional counting. Appl Radiat Isot 126:9–12.  https://doi.org/10.1016/j.apradiso.2016.12.037 CrossRefPubMedGoogle Scholar
  7. 7.
    Hoppe EW, Aalseth CE, Farmer OT, Hossbach TW, Liezers M, Miley HS, Overman NR, Reeves JH (2014) Reduction of radioactive backgrounds in electroformed copper for ultra-sensitive radiation detectors. Nucl Instrum Methods Phys Res Sect A 764:116–121.  https://doi.org/10.1016/j.nima.2014.06.082 CrossRefGoogle Scholar
  8. 8.
    Mace EK, Aalseth CE, Bonicalzi RM, Day AR, Hoppe EW, Keillor ME, Myers AW, Overman CT, Seifert A (2013) Controlling low-rate signal path microdischarge for an ultra-low-background proportional counter. J Radioanal Nucl Chem 296(2):753–758.  https://doi.org/10.1007/s10967-012-2042-1 CrossRefGoogle Scholar
  9. 9.
    Aalseth CE, Day A, Fuller E et al (2012) Digital pulse-shape discrimination applied to an ultra-low-background gas-proportional counting system: first results. J Radioanal Nucl Chem.  https://doi.org/10.1007/s10967-012-2052-z CrossRefGoogle Scholar
  10. 10.
    Hennig W, Chu YX, Tan H, Fallu-Labruyere A, Warburton WK (2007) The DGF pixie-4 spectrometer: compact digital readout electronics for HPGe clover detectors. Nuclear Instrum Methods Phys Res Sect B Beam Interact Mater Atoms 263(1):175–178.  https://doi.org/10.1016/j.nimb.2007.04.079 CrossRefGoogle Scholar
  11. 11.
    Rosenblatt F (1958) The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 65(6):386CrossRefPubMedGoogle Scholar
  12. 12.
    Anand R, Mehrotra KG, Mohan CK, Ranka S (1993) An improved algorithm for neural network classification of imbalanced training sets. IEEE Trans Neural Netw 4(6):962–969.  https://doi.org/10.1109/72.286891 CrossRefPubMedGoogle Scholar
  13. 13.
    Geman S, Bienenstock E, Doursat R (1992) Neural networks and the bias/variance dilemma. Neural Comput 4(1):1–58.  https://doi.org/10.1162/neco.1992.4.1.1 CrossRefGoogle Scholar
  14. 14.
    Hagan MT, Demuth HB, Beale MH (1996) Neural network design, vol 20. Pws Pub, BostonGoogle Scholar
  15. 15.
    Haykin S (1994) Neural networks, A comprehensive Foundation. Macmilan, BasingstokeGoogle Scholar
  16. 16.
    Wan EA (1990) Neural network classification: a Bayesian interpretation. IEEE Trans Neural Networks 1(4):303–305.  https://doi.org/10.1109/72.80269 CrossRefPubMedGoogle Scholar
  17. 17.
    Bridle JS (1990) Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition. In: Soulié FF, Hérault J (eds) Neurocomputing. Springer, Berlin Heidelberg, Berlin, Heidelberg, pp 227–236CrossRefGoogle Scholar
  18. 18.
    Goodfellow I, Bengio Y, Courville A (2016) Deep learning. The MIT Press, CambridgeGoogle Scholar
  19. 19.
    Chollet F (2015) Keras documentation. https://keras.io
  20. 20.
    Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M, Ghemawat S, Goodfellow I, Harp A, Irving G, Isard M, Jozefowicz R, Jia Y, Kaiser L, Kudlur M, Levenberg J, Mané D, Schuster M, Monga R, Moore S, Murray D, Olah C, Shlens J, Steine B, Sutskever I, Talwar K, Tucker P, Vanhoucke V, Vasudevan V, Viégas F, Vinyals O, Warden P, Wattenberg M, Wicke M, Yu Y, Zheng X (2015) TensorFlow: large-scale machine learning on heterogeneous systems. www.tensorflow.org
  21. 21.
    Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:14126980

Copyright information

© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply  2018

Authors and Affiliations

  1. 1.Pacific Northwest National LaboratoryRichlandUSA

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