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Data Analysis for Atomic Shapes in Nuclear Science

  • Mehmet Cagri KaymakEmail author
  • Hasan Metin Aktulga
  • Ron Fox
  • Sean N. Liddick
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11537)

Abstract

We consider the problem of detecting a unique experimental signature in time-series data recorded in nuclear physics experiments aimed at understanding the shape of atomic nuclei. The current method involves fitting each sample in the dataset to a given parameterized model function. However, this procedure is computationally expensive due to the nature of the nonlinear curve fitting problem. Since data is skewed towards non-unique signatures, we offer a way to filter out the majority of the uninteresting samples from the dataset by using machine learning methods. By doing so, we decrease the computational costs for detection of the unique experimental signatures in the time-series data. Also, we present a way to generate synthetic training data by estimating the distribution of the underlying parameters of the model function with Kernel Density Estimation. The new workflow that leverages machine learned classifiers trained on the synthetic data are shown to significantly outperform the current procedures used in actual datasets.

Keywords

Machine learning Density estimation Nuclear physics 

References

  1. 1.
    Balmer, M.J., Gamage, K.A., Taylor, G.C.: Comparative analysis of pulse shape discrimination methods in a 6li loaded plastic scintillator. Nucl. Instrum. Methods Phys. Res. Sect. A 788, 146–153 (2015)CrossRefGoogle Scholar
  2. 2.
    Bauer, S., Köhler, S., Doll, K., Brunsmann, U.: FPGA-GPU architecture for kernel SVM pedestrian detection. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, pp. 61–68. IEEE (2010)Google Scholar
  3. 3.
    Breiman, L., Friedman, J., Olshen, R., Stone, C.J.: Classification and regression trees. Wadsworth statistics/probability series (1984)Google Scholar
  4. 4.
    Chawla, N.V.: C4.5 and imbalanced data sets: investigating the effect of sampling method, probabilistic estimate, and decision tree structure. In: Proceedings of the ICML, vol. 3, pp. 66 (2003)Google Scholar
  5. 5.
    Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Friedman, J.H.: Stochastic gradient boosting. Comput. Stat. Data Anal. 38(4), 367–378 (2002)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)Google Scholar
  8. 8.
    Graves, A., Mohamed, A.R., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, speech and signal processing (ICASSP), pp. 6645–6649. IEEE (2013)Google Scholar
  9. 9.
    Guest, D., Cranmer, K., Whiteson, D.: Deep learning and its application to LHC physics. Ann. Rev. Nucl. Part. Sci. 68, 161–181 (2018)CrossRefGoogle Scholar
  10. 10.
    Heyde, K., Wood, J.L.: Shape coexistence in atomic nuclei. Rev. Mod. Phys. 83(4), 1467 (2011)CrossRefGoogle Scholar
  11. 11.
    Holl, P., Hauertmann, L., Majorovits, B., Schulz, O., Schuster, M., Zsigmond, A.: Deep learning based pulse shape discrimination for germanium detectors. arXiv preprint arXiv:1903.01462 (2019)
  12. 12.
    Ko, T., Peddinti, V., Povey, D., Seltzer, M.L., Khudanpur, S.: A study on data augmentation of reverberant speech for robust speech recognition. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5220–5224. IEEE (2017)Google Scholar
  13. 13.
    Kuchera, M.P., et al.: Machine learning methods for track classification in the AT-TPC. arXiv preprint arXiv:1810.10350 (2018)
  14. 14.
    Liaw, A., Wiener, M., et al.: Classification and regression by randomforest. R news 2(3), 18–22 (2002)Google Scholar
  15. 15.
    Mishina, Y., Murata, R., Yamauchi, Y., Yamashita, T., Fujiyoshi, H.: Boosted random forest. IEICE Trans. Inf. Syst. 98(9), 1630–1636 (2015)CrossRefGoogle Scholar
  16. 16.
    Moré, J.J.: The levenberg-marquardt algorithm: implementation and theory. In: Watson, G.A. (ed.) Numerical Analysis. LNM, vol. 630, pp. 105–116. Springer, Heidelberg (1978).  https://doi.org/10.1007/BFb0067700CrossRefGoogle Scholar
  17. 17.
    Palaz, D., Magimai-Doss, M., Collobert, R.: Analysis of CNN-based speech recognition system using raw speech as input. In: Sixteenth Annual Conference of the International Speech Communication Association (2015)Google Scholar
  18. 18.
    Richardson, E., Sela, M., Kimmel, R.: 3D face reconstruction by learning from synthetic data. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 460–469. IEEE (2016)Google Scholar
  19. 19.
    Rosenberg, A.: Classifying skewed data: importance weighting to optimize average recall. In: Thirteenth Annual Conference of the International Speech Communication Association (2012)Google Scholar
  20. 20.
    Sanderson, T., Scott, C., Flaska, M., Polack, J., Pozzi, S.: Machine learning for digital pulse shape discrimination. In: 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), pp. 199–202. IEEE (2012)Google Scholar
  21. 21.
    Scott, D.W.: On optimal and data-based histograms. Biometrika 66(3), 605–610 (1979)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Silverman, B.W.: Density Estimation for Statistics and Data Analysis. Routledge, Boca Raton (2018)CrossRefGoogle Scholar
  23. 23.
    Suykens, J.A., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999)CrossRefGoogle Scholar
  24. 24.
    Van Essen, B., Macaraeg, C., Gokhale, M., Prenger, R.: Accelerating a random forest classifier: Multi-core, GP-GPU, or FPGA? In: 2012 IEEE 20th International Symposium on Field-Programmable Custom Computing Machines, pp. 232–239. IEEE (2012)Google Scholar
  25. 25.
    Yang, J., Nguyen, M.N., San, P.P., Li, X., Krishnaswamy, S.: Deep convolutional neural networks on multichannel time series for human activity recognition. In: IJCAI, vol. 15, pp. 3995–4001 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mehmet Cagri Kaymak
    • 1
    Email author
  • Hasan Metin Aktulga
    • 1
  • Ron Fox
    • 2
  • Sean N. Liddick
    • 2
    • 3
  1. 1.Department of Computer Science and EngineeringMichigan State UniversityEast LansingUSA
  2. 2.National Superconducting Cyclotron LaboratoryMichigan State UniversityEast LansingUSA
  3. 3.Department of ChemistryMichigan State UniversityEast LansingUSA

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