Application of Neural Networks to Classification of Data of the TUS Orbital Telescope
Abstract
:1. Introduction
2. The TUS Detector
3. Phenomenological Classification of the TUS Data
- events with stationary noise-like waveforms; these included events with a strongly non-uniform illumination of the focal surface with bright regions correlated with geographical positions of cities and objects such as airports, power plants, offshore platforms etc.
- instant track-like flashes (TLFs) caused by charged particles hitting the UV filters of the photodetector;
- flashes produced by light coming outside of the FOV of the detector and scattered on its mirror; they were called “slow flashes” because of the long signal rise time in comparison with TLFs; and
- events with complex spatio-temporal dynamics; these included so-called ELVEs, which are short-lived optical events that manifest at the lower edge of the ionosphere (altitudes of ∼90 km) as bright rings expanding at the speed of light up to a maximum radius of ∼300 km [34], events with waveforms that could be expected from fluorescence originating from extensive air showers produced by extreme energy cosmic rays, as well as violent flashes of a yet unknown origin.
3.1. Instant Track-Like Flashes
3.2. Slow Flashes
4. Application of Neural Networks
4.1. Instant Track-Like Flashes
4.2. Slow Flashes
5. Discussion
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
1 | https://heasarc.gsfc.nasa.gov/docs/rosat/gallery/misc_saad.html, accessed on 22 June 2021. |
2 | https://ngdc.noaa.gov/eog/dmsp/downloadV4composites.html, accessed on 31 May 2021. |
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2-Layer MLPs. # Nodes: | 4 | 8 | 16 | 24 | 32 | 48 | CNN |
---|---|---|---|---|---|---|---|
Training accuracy | 0.9983 | 0.9991 | 0.9993 | 0.9995 | 0.9996 | 0.9997 | 1.0 |
Best validation accuracy | 0.9954 | 0.9960 | 0.9969 | 0.9966 | 0.9969 | 0.9972 | 0.9976 |
Testing accuracy | 0.9954 | 0.9961 | 0.9969 | 0.9973 | 0.9974 | 0.9975 | 0.9973 |
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Zotov, M. Application of Neural Networks to Classification of Data of the TUS Orbital Telescope. Universe 2021, 7, 221. https://doi.org/10.3390/universe7070221
Zotov M. Application of Neural Networks to Classification of Data of the TUS Orbital Telescope. Universe. 2021; 7(7):221. https://doi.org/10.3390/universe7070221
Chicago/Turabian StyleZotov, Mikhail. 2021. "Application of Neural Networks to Classification of Data of the TUS Orbital Telescope" Universe 7, no. 7: 221. https://doi.org/10.3390/universe7070221