Neural Network Based Approach to Recognition of Meteor Tracks in the Mini-EUSO Telescope Data
Abstract
:1. Introduction
2. Mini-EUSO Experiment
3. Meteor and Background Signals
- A signal produced by a meteor in a pixel has a shape resembling the bell-like curve similar to the probability density function of the normal distribution.
- Meteor signals produce quasi-linear tracks in the focal surface.
- The number of hit (“active”) pixels in more than 75% of meteor tracks is ≤5, so that their footprints on the focal surface are small.
- Peaks of a meteor signal shift from one pixel to another (except for arrival directions close to nadir).
- There are multiple signals in the data with the shape similar to that of meteors but simultaneously illuminating large areas of the FS.
- Meteors are often registered on strong and quickly varying background illumination.
- The amplitude of a meteor signal is typically lower than amplitudes of some other signals in the FoV of Mini-EUSO registered simultaneously with the meteor.
- In some cases, it is impossible to judge unequivocally if a signal originated from a meteor or another source.
4. Results
4.1. Recognition of Meteor Data Samples
4.2. Active Pixel Selection
5. Discussion
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
ANN | Artificial neural network |
AUC | Area under the curve |
CNN | Convolutional neural network |
EC | Elementary cell |
FoV | Field of view |
FS | Focal surface |
ISS | International space station |
MAPMT | Multi-anode photomultiplier |
MCC | Matthews correlation coefficient |
MLP | Multi-layer perceptron |
PR | Precision-recall |
PSF | Point spread function |
ROC | Receiver operating characteristic |
UHECR | Ultra-high energy cosmic ray |
UV | ultraviolet |
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Test Session | 5 | 6 | 7 | 8 | 11 | 12 | 13 | 14 |
---|---|---|---|---|---|---|---|---|
Training meteor chunks | 91,060 | 72,124 | 95,924 | 81,188 | 80,724 | 88,456 | 89,228 | 86,820 |
Testing meteor chunks | 1712 | 6446 | 474 | 4180 | 4274 | 2341 | 2148 | 2772 |
Testing meteors | 65 | 280 | 18 | 186 | 193 | 106 | 90 | 130 |
Test Session | 5 | 6 | 7 | 8 | 11 | 12 | 13 | 14 |
---|---|---|---|---|---|---|---|---|
ROC AUC | 0.992 | 0.994 | 0.999 | 0.993 | 0.994 | 0.998 | 0.993 | 0.996 |
PR AUC | 0.937 | 0.955 | 0.876 | 0.933 | 0.946 | 0.973 | 0.931 | 0.956 |
MCC | 0.872 | 0.894 | 0.732 | 0.857 | 0.888 | 0.921 | 0.782 | 0.901 |
F1 | 0.873 | 0.901 | 0.718 | 0.863 | 0.892 | 0.922 | 0.776 | 0.904 |
FNR (met) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Test Session | 5 | 6 | 7 | 8 | 11 | 12 | 13 | 14 |
---|---|---|---|---|---|---|---|---|
ROC AUC | 0.992 | 0.995 | 0.993 | 0.994 | 0.996 | 0.993 | 0.995 | 0.995 |
PR AUC | 0.899 | 0.932 | 0.877 | 0.916 | 0.932 | 0.887 | 0.936 | 0.928 |
MCC | 0.790 | 0.841 | 0.744 | 0.826 | 0.847 | 0.812 | 0.809 | 0.835 |
F1 | 0.794 | 0.845 | 0.737 | 0.832 | 0.852 | 0.814 | 0.810 | 0.840 |
Mean IoU | 0.808 | 0.853 | 0.773 | 0.843 | 0.861 | 0.829 | 0.825 | 0.850 |
FNR (pxl) | 2/422 | 2/1428 | 0/80 | 7/928 | 5/958 | 0/492 | 0/457 | 2/630 |
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Share and Cite
Zotov, M.; Anzhiganov, D.; Kryazhenkov, A.; Barghini, D.; Battisti, M.; Belov, A.; Bertaina, M.; Bianciotto, M.; Bisconti, F.; Blaksley, C.; et al. Neural Network Based Approach to Recognition of Meteor Tracks in the Mini-EUSO Telescope Data. Algorithms 2023, 16, 448. https://doi.org/10.3390/a16090448
Zotov M, Anzhiganov D, Kryazhenkov A, Barghini D, Battisti M, Belov A, Bertaina M, Bianciotto M, Bisconti F, Blaksley C, et al. Neural Network Based Approach to Recognition of Meteor Tracks in the Mini-EUSO Telescope Data. Algorithms. 2023; 16(9):448. https://doi.org/10.3390/a16090448
Chicago/Turabian StyleZotov, Mikhail, Dmitry Anzhiganov, Aleksandr Kryazhenkov, Dario Barghini, Matteo Battisti, Alexander Belov, Mario Bertaina, Marta Bianciotto, Francesca Bisconti, Carl Blaksley, and et al. 2023. "Neural Network Based Approach to Recognition of Meteor Tracks in the Mini-EUSO Telescope Data" Algorithms 16, no. 9: 448. https://doi.org/10.3390/a16090448