# Neural Network Based Approach to Recognition of Meteor Tracks in the Mini-EUSO Telescope Data
## 3 Meteor and Background Signals
A conventional way to find meteor signals in the Mini-EUSO or TUS data would be to look for signals that can be fitted with the probability density function of a Gaussian distribution or its sum with a polynomial in case of non-stationary background illumination [21; 22; 28]. The known range of possible speeds of meteors (11-72 km s\({}^{-1}\)) together with information on the orbital speed of the ISS (\(\sim\)7.7 km s\({}^{-1}\)), the FoV of one pixel, and the PSF size allows one to estimate the variance of a Gaussian fit. This also allows for verifying the kinematics of a signal moving across the FS. The latter step is of crucial importance since there are multiple signals in the Mini-EUSO data that can be fitted with a Gaussian distribution but take place simultaneously in big pixel groups without forming a track on the focal surface. The task of recognizing meteor patterns in the Mini-EUSO dataset with machine learning methods can be considered as a binary classification problem since one basically needs to separate meteor signals from all the rest. Seemingly the most obvious way to tackle the task with artificial neural networks is to employ supervised learning. Within this approach, one can train an ANN using a labeled dataset. The dataset can be either  
Figure 2: A typical meteor signal registered by Mini-EUSO. (**Top left**): signals in pixels that constitute the meteor signal. (**Top right**): location of meteor pixels in the focal surface. Colors denote a time shift of the peaks with respect to the first one (in units of D3 GTUs). (**Bottom left**): all signals registered by Mini-EUSO simultaneously with the meteor. The black curves show the meteor signal. (**Bottom right**): a snapshot of the focal surface made at the moment of the brightest meteor signal (GTU 184). prepared by simulations or extracted from real experimental data. It is tempting to choose the first way since meteor signals mostly have a bell curve shape similar to the density of the normal distribution. However, realistic simulations are not trivial since the background illumination is diverse, and sensitivity of different pixels on the FS is not known. This made us adopt the second approach. We took two meteor datasets obtained by the JEM-EUSO collaboration and complemented them by our own analysis to prepare a dataset suitable for training and testing an ANN. It is necessary to stress once again that the source of a considerable number of bell curve-like signals registered with Mini-EUSO cannot be identified with confidence. Signals like those shown in Figures 1 and 2 do not pose a problem in this respect. However, the shape and kinematics of tracks produced by meteors consisting of \(\leq\)4 pixels are often confusing. Another difficulty arises from dim signals on a strong and varying background. As a result, it is impossible to obtain ground-truth labels basing exclusively on the existing data set. After several tests, we confined the labeled dataset to signals the nature of which causes little doubt. In particular, we excluded almost all signals occupying two pixels. The resulting dataset used for training and testing ANNs discussed below consisted of 1068 meteor signals. Every record in the dataset included a timestamp of a meteor, coordinates of active pixels on the focal surface, and positions of the respective signal peaks. Since data obtained in the D3 mode do not have a trigger but are similar to a series of videos each consisting of thousands of \"frames\" representing \"snapshots\" of the FS, there is a question how to extract samples containing meteor and non-meteor signals for training and testing datasets. For example, one can create data chunks of size \(48\times 48\times T\), where \(T\) is the number of time frames (D3 GTUs) large enough to fit all meteors in the dataset and either center them on meteor peaks or put them in a fixed position with respect to the beginning of a meteor signal. This will allow one to obtain a \"unified\" representation of meteor signals to an ANN thus simplifying the task of their recognition.