# Neural Network Based Approach to Recognition of Meteor Tracks in the Mini-EUSO Telescope Data
## 5 Discussion  
We have demonstrated that a pipeline made of two simple neural networks, a CNN and an MLP, can be used to effectively recognize meteor tracks in the data of the Mini-EUSO fluorescence telescope that is observing the nocturnal atmosphere of Earth in the UV band from the International Space Station. The CNN used to select three-dimensional data chunks containing meteor signals managed to recognize properly all 1068 meteors in the dataset. The MLP employed to recognize pixels with meteor signals in data chunks picked up by the CNN, reached an accuracy beyond 99%. Neither of the ANNs puts high demands on computer resources necessary for their training. Besides this, they perform surprisingly fast during the classification stage with a major part of time being spent on reading data from a data storage, thus strongly outperforming the conventional algorithm. We have seen in [33] that an ANN trained on data with clearly pronounced signals is able to identify patterns with low signal-to-noise ratio. Such events are classified as false positives during tests but their closer analysis reveals that their considerable part contains \"positive\" signals that were not found by the conventional algorithm used to prepare training and testing datasets. A preliminary analysis of signals marked as false positives in our tests has demonstrated that the same situation takes place with meteor tracks, so that the list of meteors can be extended with these newly found signals. The same applies to the list of active pixels. This is an important advantage of ML-based methods over conventional approaches. One can anticipate that new experimental data might present new patterns of non-meteor signals since observational conditions vary strongly from one session to another. In this case, it might be necessary to extend the training dataset and retrain the ANNs. However, we do not expect that the architecture of the CNN and the MLP will need to be modified considerably. On the other hand, it is clear that the presented ANNs are not the only way of recognizing meteor tracks in the Mini-EUSO data using methods \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Test Session** & **5** & **6** & **7** & **8** & **11** & **12** & **13** & **14** \\ \hline 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 \\ FI & 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 \\ \hline FNR (pxl) & 2/422 & 2/1428 & 0/80 & 7/928 & 5/958 & 0/492 & 0/457 & 2/630 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of the MLP on different sessions of observations. See the text for details. of machine learning. Still, it might be not easy to exceed the accuracy of the suggested pipeline with simple models. We plan to analyze other possible approaches, especially for the segmentation part. We are also going to address the task of solving the same problem in one step, without splitting it into two parts. Finally, it is worth mentioning that the presented method is not confined to recognizing meteor tracks. Preliminary results of applying the same approach and even the same trained models for recognizing signals that mimic illumination expected from extensive air showers born by ultra-high energy cosmic rays, are quite promising and will be reported elsewhere. We also expect that this pipeline or a similar one can be implemented in onboard electronics of future orbital missions to act as a trigger for track-like signals of different nature manifesting themselves at various time scales. Conceptualization, M.B. ([PERSON]), M.C., T.E., P.G., P.K., T.N., A.O., E.P., P.P., M.R. and L.W. ; methodology, M.Z. ; formal analysis, D.A. and A.K. ; investigation, M.Z. ; resources, M.B. ([PERSON]), A.B., M.B.