# Neural Network Based Approach to Recognition of Meteor Tracks in the Mini-EUSO Telescope Data
## 4 Results
### Recognition of Meteor Data Samples  
One of the crucial questions to be solved before training an ANN is how to prepare and organize input data. In our case, the question is twofold. We need to decide how to label three-dimensional chunks into those that contain a signal of a meteor and those that do not. Besides this, we need to choose the size of data chunks \(P\times P\times T\), where \(P\times P\) defines the size of a square on the focal surface (measured in pixels), and \(T\) is the number of time frames (\"snapshots\" of the FS). A data chunk was labeled as containing a meteor signal in case there were at least two meteor pixels inside a \(P\times P\) area with peaks of the signals located within \(T\) GTUs. The reason is that we do not have a straightforward way to decide if a signal originates from a meteor with just a single active pixel. On the other hand, putting a more strict cut on the number of active pixels inside a chunk (\(\geq\)3) results in a loss of short meteor tracks that have only two active pixels. As for the number of time frames in data chunks, we tested \(T=8,16,32,48,64\), and 128, which covers the range of duration of meteor signals in the dataset. Values \(T=32\), 48, and 64 demonstrated the best results in our tests in terms of all metrics mentioned above with different combinations of training and testing sessions regardless of the value of \(P\)with \(T=48\) showing in average marginally better performance than the other two values. This value is used in all figures and tables presented below. In [33], a simple convolutional neural network (CNN) was employed to perform binary classification of two types of signals registered with the TUS telescope. The instrument had a focal surface of \(16\times 16\) pixels, and data arranged in \(16\times 16\times T\) chunks worked well. Thus, we first tried training a CNN for Mini-EUSO with data chunks of the size \(48\times 48\times T\). The input data was standardized according to the formula \((X_{i}-\langle X_{i}\rangle)/\sigma(X_{i})\), where \(X_{i}\) is the signal in pixel \(i\), \(\langle X_{i}\rangle\) and \(\sigma(X_{i})\) are estimations of the mean and the standard deviation during \(T\) time frames. However, as it was briefly reported in [23], this approach did not allow us to obtain acceptable results. We tested numerous configurations of CNNs and long short-term memory networks but failed to obtain ROC AUC \(>0.75\) on testing datasets. A simple solution was found by splitting the FS into smaller squares. We tested splitting with \(P=24,16,12,8\), and \(6\). In order to avoid losing signals around the boundaries of these small areas, we used overlapping by \(P/2\) pixels in both directions. Figure 3 shows the behavior of mean values of different performance metrics for varying \(P\) with models trained on all possible combinations of seven sessions and tested on the remaining one. In this case, the same architecture of a CNN was used for all tests. It can be seen that performance expressed in terms of any metric increases quickly for \(P<24\). The PR AUC, the MCC, and the \(F_{1}\) score change in a very similar fashion while the values of the ROC AUC are close to those of \(1-\) FNR (met) for small \(P\). The best performance is reached for \(P=8\) with the MCC and \(F_{1}\) metrics slightly decreasing for \(P=6\). Thus, data chunks of the size \(8\times 8\times 48\) are used in what follows. A CNN that we adopted for classifying meteor chunks consists of a convolutional layer with 24 filters and a kernel of size 3. It utilizes ReLU as an activation function and the L2 kernel regularizer with factor 0.1. The convolutional layer was followed by a maxpooling and dropout layers and two fully connected layers with 256 and then 64 neurons. Adam was used as an optimization algorithm. Sigmoid was employed as an activation function in the output layer.