Abstract
We report on the development of neural networks for generating readings from Telescope Array’s surface detectors with the largest registered integral signal. To achieve this goal, we implemented generative Wasserstein adversarial networks with the gradient penalty. The data used to train the model was generated using the Monte Carlo method. We obtained visually similar data which are consistent with the physics of the underlying processes. The anomaly search method can be employed to identify discrepancies between real and simulated data, as well as to introduce a quantitative measure of similarity between the real detector readings and those generated by the neural network’s readings.
REFERENCES
T. Abu-Zayyad, R. Aida, M. Allen, et al., Nucl. Instrum. Methods Phys. Res., Sect. A 689, 87 (2012). https://doi.org/10.1016/j.nima.2012.05.079
I. Allekotte, A. F. Barbosa, P. Bauleo, et al., Nucl. Instrum. Methods Phys. Res., Sect. A 586, 409 (2008). https://doi.org/10.1016/j.nima.2007.12.016
H. He, Radiat. Detect. Technol. Methods 2, 7 (2018). https://doi.org/10.1007/s41605-018-0037-3
L. A. Kuzmichev, I. I. Astapov, P. A. Bezyazeekov, et al., Phys. At. Nucl. 81, 497 (2018). https://doi.org/10.1134/S1063778818040105
T. Abu-Zayyad, K. Belov, D. J. Bird, et al., Phys. Rev. Lett. 84, 4276 (2000). https://doi.org/10.1103/PhysRevLett.84.4276
Yu. A. Fomin, N. N. Kalmykov, I. S. Karpikov, et al., Astropart. Phys. 92, 1 (2017). https://doi.org/10.1016/j.astropartphys.2017.04.001
H. Dembinski, EPJ Web Conf. 145, 18003 (2017). https://doi.org/10.1051/epjconf/201714518003
J. Harvey and R. Rockwell, Opt. Eng. 27, 279762 (1988). https://doi.org/10.1117/12.7976758
I. Goodfellow, J. Pouget-Abadie, M. Mirza, et al., in Advances in Neural Information Processing Systems (NIPS 2014), Vol. 27, p. 2672.
G. R. Khattak, S. Vallecorsa, F. Carminati, and G. M. Khan, Eur. Phys. J. C 82, 386 (2022). https://doi.org/10.1140/epjc/s10052-022-10258-4
F. Ratnikov, EPJ Web Conf. 245, 7 (2020). https://doi.org/10.1051/epjconf/202024502026
M. Arjovsky, S. Chintala, and L. Bottou, in Proc. 34th Int. Conf. on Machine Learning, Sydney, 2017, (New York, 2017), p. 214.
I. Gulrajani, F. Ahmed, M. Arjovsky, et al., in Advances in Neural Information Processing Systems (NIPS 2017), Vol. 30.
D. Heck, J. Knapp, J. N. Capdevielle, et al., Report FZKA-6019 (1998).
S. Ostapchenko, Nucl. Phys. B Proc. Suppl. 151, 143 (2006). https://doi.org/10.1016/j.nuclphysbps.2005.07.026
T. Schlegl, P. Seeböck, S. M. Waldstein, et al., in Int. Conf. on Information Processing in Medical Imaging (Boone, N.C., 2017), p. 146.
Funding
The work was supported by the Russian Science Foundation, grant no. 22-22-20063.
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
The authors of this work declare that they have no conflicts of interest.
Additional information
Publisher’s Note.
Allerton Press remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Fitagdinov, R.R., Kharuk, I.V. Generation of the Ground Detector Readings of the Telescope Array Experiment and the Search for Anomalies Using Neural Networks. Moscow Univ. Phys. 78 (Suppl 1), S59–S63 (2023). https://doi.org/10.3103/S0027134923070068
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.3103/S0027134923070068