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Tackling the muon identification in water Cherenkov detectors problem for the future Southern Wide-field Gamma-ray Observatory by means of machine learning

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Abstract

This paper presents several approaches to deal with the problem of identifying muons in a water Cherenkov detector with a reduced water volume and 4 PMTs. Different perspectives of information representation are used, and new features are engineered using the specific domain knowledge. As results show, these new features, in combination with the convolutional layers, are able to achieve a good performance avoiding overfitting and being able to generalise properly for the test set. The results also prove that the combination of state-of-the-art machine learning analysis techniques and water Cherenkov detectors with low water depth can be used to efficiently identify muons, which may lead to huge investment savings due to the reduction of the amount of water needed at high altitudes. This achievement can be used in further research to be able to discriminate between gamma and hadron-induced showers using muons as discriminant.

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Notes

  1. 1 Gigaelectronvolt (GeV) = \(10^{9}.\) eV.

    1 Teraelectronvolt (TeV) = \(10^{12}.\) eV.

  2. https://github.com/pacocp/spocu-pytorch.

  3. https://keras.io/api/optimizers/.

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Acknowledgements

We would like to thank to A. Bueno for all the support and useful discussions during the development of this work. The authors thank also for the financial support by OE - Portugal, FCT, I. P., under project PTDC/FIS-PAR/29158/2017. R. C. is grateful for the financial support by OE-Portugal, FCT, I. P., under DL57 /2016/cP1330/cT0002. A. G. is grateful for the financial support by the projects MINECO FPA2017-85197-P and PID2019-104676GB-C32. B.S.G. is grateful for the financial support by grant LIP/BI - 14/2020, under project IC&DT, POCI-01-0145-FEDER-029158.

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Authors and Affiliations

  1. LIP, Av. Prof. Gama Pinto, 2, 1649-003, Lisbon, Portugal

    B. S. González, R. Conceição, M. Pimenta & B. Tomé

  2. Instituto Superior Técnico (IST), Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001, Lisbon, Portugal

    B. S. González, R. Conceição, M. Pimenta & B. Tomé

  3. Computer Architecture and Technology Department, University of Granada, Granada, Spain

    B. S. González & A. Guillén

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  1. B. S. González
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  2. R. Conceição
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Correspondence to B. S. González.

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González, B.S., Conceição, R., Pimenta, M. et al. Tackling the muon identification in water Cherenkov detectors problem for the future Southern Wide-field Gamma-ray Observatory by means of machine learning. Neural Comput & Applic 34, 5715–5728 (2022). https://doi.org/10.1007/s00521-021-06730-z

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