3 research outputs found
Machine Learning for Mini-EUSO Telescope Data Analysis
Neural networks as well as other methods of machine learning (ML) are known
to be highly efficient in different classification tasks, including
classification of images and videos. Mini- EUSO is a wide-field-of-view imaging
telescope that operates onboard the International Space Station since 2019
collecting data on miscellaneous processes that take place in the atmosphere of
Earth in the UV range. Here we briefly present our results on the development
of ML-based approaches for recognition and classification of track-like signals
in the Mini-EUSO data, among them meteors, space debris and signals the light
curves and kinematics of which are similar to those expected from extensive air
showers generated by ultra-high-energy cosmic rays. We show that even simple
neural networks demonstrate impressive performance in solving these tasks.Comment: 10 pages, 3 figures, ICRC2023 conferenc
Neural Network Based Approach to Recognition of Meteor Tracks in the Mini-EUSO Telescope Data
Mini-EUSO is a wide-angle fluorescence telescope that registers ultraviolet
(UV) radiation in the nocturnal atmosphere of Earth from the International
Space Station. Meteors are among multiple phenomena that manifest themselves
not only in the visible range but also in the UV. We present two simple
artificial neural networks that allow for recognizing meteor signals in the
Mini-EUSO data with high accuracy in terms of a binary classification problem.
We expect that similar architectures can be effectively used for signal
recognition in other fluorescence telescopes, regardless of the nature of the
signal. Due to their simplicity, the networks can be implemented in onboard
electronics of future orbital or balloon experiments.Comment: 15 page
Neural Network Based Approach to Recognition of Meteor Tracks in the Mini-EUSO Telescope Data
Mini-EUSO is a wide-angle fluorescence telescope that registers ultraviolet (UV) radiation in the nocturnal atmosphere of Earth from the International Space Station. Meteors are among multiple phenomena that manifest themselves not only in the visible range but also in the UV. We present two simple artificial neural networks that allow for recognizing meteor signals in the Mini-EUSO data with high accuracy in terms of a binary classification problem. We expect that similar architectures can be effectively used for signal recognition in other fluorescence telescopes, regardless of the nature of the signal. Due to their simplicity, the networks can be implemented in onboard electronics of future orbital or balloon experiments