Application of neural networks to classification of data of the TUS orbital telescope

We employ neural networks for classification of data of the TUS fluorescence telescope, the world's first orbital detector of ultra-high energy cosmic rays. We focus on two particular types of signals in the TUS data: track-like flashes produced by cosmic ray hits of the photodetector and flashes that originated from distant lightnings. We demonstrate that even simple neural networks combined with certain conventional methods of data analysis can be highly effective in tasks of classification of data of fluorescence telescopes.Comment: 24 pages, multiple figures; v2: minor modifications to address reviewer's comment

A Neural Network Approach for Selecting Track-like Events in Fluorescence Telescope Data

In 2016-2017, TUS, the world's first experiment for testing the possibility of registering ultra-high energy cosmic rays (UHECRs) by their fluorescent radiation in the night atmosphere of Earth was carried out. Since 2019, the Russian-Italian fluorescence telescope (FT) Mini-EUSO ("UV Atmosphere") has been operating on the ISS. The stratospheric experiment EUSO-SPB2, which will employ an FT for registering UHECRs, is planned for 2023. We show how a simple convolutional neural network can be effectively used to find track-like events in the variety of data obtained with such instruments.Comment: 5 pages, to be published in proceedings of the 37th Russian Cosmic Ray Conference (2022

Hecke Transformations of Conformal Blocks in WZW Theory. I. KZB Equations for Non-Trivial Bundles

We describe new families of the Knizhnik-Zamolodchikov-Bernard (KZB) equations related to the WZW-theory corresponding to the adjoint $G$-bundles of different topological types over complex curves $\Sigma_{g,n}$ of genus $g$ with $n$ marked points. The bundles are defined by their characteristic classes - elements of $H^2(\Sigma_{g,n},\mathcal{Z}(G))$, where $\mathcal{Z}(G)$ is a center of the simple complex Lie group $G$. The KZB equations are the horizontality condition for the projectively flat connection (the KZB connection) defined on the bundle of conformal blocks over the moduli space of curves. The space of conformal blocks has been known to be decomposed into a few sectors corresponding to the characteristic classes of the underlying bundles. The KZB connection preserves these sectors. In this paper we construct the connection explicitly for elliptic curves with marked points and prove its flatness

Search for Extreme Energy Cosmic Rays with the TUS orbital telescope and comparison with ESAF

The Tracking Ultraviolet Setup (TUS) detector was launched on April 28, 2016 as a part of the scientific payload of the Lomonosov satellite. TUS is a pathfinder mission for future space-based observation of Extreme-Energy Cosmic Rays (EECRs, E > 5x1019 eV) with experiments such as K-EUSO. TUS data offer the opportunity to develop strategies in the analysis and reconstruction of the events which will be essential for future space-based missions. During its operation, TUS has detected about 80 thousand events which have been subject to an offline analysis to select among them those that satisfy basic temporal and spatial criteria of EECRs. A few events passed this first screening. In order to perform a deeper analysis of such candidates, a dedicated version of ESAF (EUSO Simulation and Analysis Framework) code as well as a detailed modelling of TUS optics and detector are being developed

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

UV telescope TUS on board Lomonosov satellite: Selected results of the mission

The Tracking Ultraviolet Setup (TUS) was the first orbital detector aimed to check the possibility of recording ultra-high energy cosmic rays (UHECRs) at E≳100 EeV by measuring the fluorescence signal of extensive air showers in the atmosphere. TUS was an experiment funded by the Russian Space Agency ROSCOSMOS, and it operated as a part of the scientific payload of the Lomonosov satellite since April 2016 till late 2017. During its mission, TUS registered almost 80,000 events in its main operation mode, with a few of them being sufficiently interesting to be more deeply scrutinized as they showed light profile and duration similar to UHECR events, even though much brighter. At the same time, the data acquired by TUS in different acquisition modes have been used to search for more exotic matter such us strangelets and nuclearites, and to measure occurrence, time profile and signal amplitude of different classes of transient luminous events among other scientific objectives, showing the interdisciplinary capability of a space-based observatory for UHECRs. In this paper, we report a selection of studies and results obtained with the TUS telescope which will be presented and placed in the contest of the present and future missions dedicated to the observation of UHECRs from space such as Mini-EUSO, K-EUSO and POEMMA