Skip to main content
SpringerLink
Log in

Simulation and Analysis of the Properties of Linear Structures in the Mass Distribution of Nuclear Reaction Products by Machine Learning Methods

  • PHYSICS OF ELEMENTARY PARTICLES AND ATOMIC NUCLEI. EXPERIMENT
  • Published:
Physics of Particles and Nuclei Letters Aims and scope Submit manuscript

Abstract

This paper is devoted to the analysis of manifestations of clustering in rare multibody decays of heavy nuclei. A computer model of the fine structure was developed jointly with the physicists of FLNR JINR; it was found based on experiments with the transuranium element Californium. To test the hypothesis that the structure really exists and is not a noise artifact, it was proposed to use a deep convolution network as a binary classifier trained on a large sample of model and noise images. Preliminary results of using the developed neuroclassifier show the prospects for this approach.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.
Fig. 9.
Fig. 10.
Fig. 11.
Fig. 12.
Fig.13.
Fig. 14.
Fig. 15.

Similar content being viewed by others

REFERENCES

  1. Yu. V. Pyatkov et al., Nucl. Phys. A 611, 355–369 (1996).

    Article  ADS  Google Scholar 

  2. Yu. V. Pyatkov et al., Nucl. Phys. A 624, 140 (1997).

    Article  ADS  Google Scholar 

  3. Yu. V. Pyatkov et al., Nucl. Instrum. Methods Phys. Res., Sect. A 488, 381 (2002).

    Google Scholar 

  4. Yu. V. Pyatkov et al., Phys. At. Nucl. 67, 1726 (2004).

    Article  Google Scholar 

  5. Yu. V. Pyatkov et al., Pattern Recogn. Image Anal. 21, 82–87 (2011).

    Article  Google Scholar 

  6. Yu. V. Pyatkov et al., Eur. Phys. J. A 48, 94 (2012).

    Article  ADS  Google Scholar 

  7. P. V. C. Hough, “A method and means for recognizing complex patterns,” US Patent No. 3069654 (1962).

  8. Yu. V. Pyatkov et al., in Proceedings of the 26th International Seminar on Interaction of Neutrons with Nuclei, Xi’an, China, May 28–01 June, 2018, pp. 285–290.

  9. B. D. Wilkins et al., Phys. Rev. C 14, 1832 (1976).

    Article  ADS  Google Scholar 

  10. V. A. Nikitin and G. A. Ososkov, Automation of Measurements and Data Processing of a Physical Experiment (Mosk. Gos. Univ., Moscow, 1986) [in Russian].

    Google Scholar 

  11. S. G. Dmitrievskii and G. A. Ososkov, Mathematical Modeling, Part 1, The School-Book (Univ. Dubna, Dubna, 2011) [in Russian].

    Google Scholar 

  12. A. Kadurin, E. Arkhangel’skaya, and S. Nikolenko, Deep Learning. Dive into the World of Neural Networks (Piter, St. Petersburg, 2018) [in Russian].

    Google Scholar 

  13. Understanding Categorical Cross-Entropy Loss, Binary Cross-Entropy Loss, Softmax Loss, Logistic Loss, Focal Loss and all those confusing names. https://gombru.github.io/2018/05/23/cross_entropy_loss/. Accessed November 10, 2019.

  14. D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv: 1412.6980 [cs.LG].

Download references

Author information

Authors and Affiliations

  1. Joint Institute for Nuclear Research, 141980, Dubna, Russia

    G. A. Ososkov, Yu. V. Pyatkov & M. O. Rudenko

  2. National Research Nuclear University Moscow Engineering Physics Institute, 115409, Moscow, Russia

    Yu. V. Pyatkov

Authors
  1. G. A. Ososkov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu. V. Pyatkov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. O. Rudenko
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. O. Rudenko.

Additional information

Translated by E. Baldina

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ososkov, G.A., Pyatkov, Y.V. & Rudenko, M.O. Simulation and Analysis of the Properties of Linear Structures in the Mass Distribution of Nuclear Reaction Products by Machine Learning Methods. Phys. Part. Nuclei Lett. 18, 559–569 (2021). https://doi.org/10.1134/S1547477121050083

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1547477121050083

Search

Navigation