RAS PhysicsЖурнал экспериментальной и теоретической физики Journal of Experimental and Theoretical Physics

  • ISSN (Print) 0044-4510
  • ISSN (Online) 3034-641X

Signal Separation from Thermal Neutrons in Electron–Neutron Detectors Using Convolutional Neural Nets in the ENDA Experiment

PII
10.31857/S0044451023040090-1
DOI
10.31857/S0044451023040090
Publication type
Article
Status
Published
Authors
Volume/ Edition
Volume 163 / Issue number 4
Pages
524-530
Abstract
The electron–neutron detector array (ENDA) is being created in China within the large high-altitude air shower observatory (LHAASO) project. The concept of the array is to simultaneously record the electromagnetic and hadronic components of extensive air showers (EAS) with EN detectors. To estimate the number of hadrons in an EAS, the array detectors record secondary thermal neutrons delayed relative to the shower front. Some of the delayed pulses are created by the simultaneous passage of several charged particles through the scintillator (the signal from one particle lies below the detection threshold) and by the photomultiplier noise. We propose a neutron pulse separation method for EN detectors using convolutional neural networks and make a comparison with the baseline method being currently applied at the installation.
Keywords
Date of publication
15.04.2023
Year of publication
2023
Number of purchasers
0
Views
33

References

  1. 1. Yu. V. Stenkin, Nucl. Phys. B Proc. Suppl. 196, 293 (2009).
  2. 2. O. B. Shchegolev, V. V. Alekseenko, D. A. Kuleshov et al., J. Phys. Conf. Ser. 1690 (2020).
  3. 3. Yu. V. Stenkin, V. V. Alekseenko, Danzengluobu et al., Bull.Russ. Acad. Sci. Phys. 85, 405 (2021).
  4. 4. О. Б. Щеголев, В. В. Алексеенко, Ю. В. Стенькин и др., Изв. РАН, сер. физ. 83, 691 (2019).
  5. 5. Ю. В. Стенькин, О. Б. Щеголев, Изв. РАН, сер. физ. 81, 541 (2017).
  6. 6. Yu. V. Stenkin, V. V. Alekseenko, D. M. Gromushkin et al., Chinese Phys. C 37, 015001 (2013).
  7. 7. G. Ranucci, Nucl. Instr. Meth. A 354, 389 (1995).
  8. 8. F. Pino, L. Stevanato, D. Cester et al., J. Instrument. 10, T08005 (2015).
  9. 9. J. K. Polack, M. Flaska, A. Enqvist et al., Nucl. Instr. Meth. A 795, 253 (2015).
  10. 10. E. Doucet, T. Brown, P. Chowdhury et al., Nucl. Instr. Meth. A 954, 161201 (2020).
  11. 11. T. S. Sanderson, C. D. Scott, M. Flaska et al., IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 199 (2012).
  12. 12. J. Gri ths, S. Kleinegesse, D. Saunders et al., Machine Learning: Science and Technology 1, 045022 (2020).
  13. 13. Д. М. Громушкин, А. А. Петрухин, Ю. В. Стенькин и др., Изв. РАН, сер. физ. 73, 425 (2009).
  14. 14. Ю. В. Стенькин, В. В. Алексеенко, А. С. Багрова и др., Изв. РАН, сер. физ. 81, 179 (2017).
  15. 15. W. S. Cleveland, American Statistician 35, 54 (1981).
  16. 16. P. Refaeilzadeh, L. Tang, and H. Liu, Encyclopedia of Database Systems 5, 532 (2009).
  17. 17. A. Paszke, S. Gross, F. Massa et al., Advances in Neural Information Processing Systems 32, 8026 (2019).
  18. 18. J. Deng, J. Guo, T. Liu et al., arXiv: 1801.07698.
  19. 19. L. Van der Maaten and G. Hinton, J. Machine Learning Res. 9, 2579 (2008).
  20. 20. D. P. Kingma and J. Ba, arXiv:1412.6980.
QR
Translate

Индексирование

Scopus

Scopus

Scopus

Crossref

Scopus

Higher Attestation Commission

At the Ministry of Education and Science of the Russian Federation

Scopus

Scientific Electronic Library