[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122698-en":3,"doc-seo-122698-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122698,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Machine Learning for Mini-EUSO Telescope Data Analysis","Neural networks and other machine learning techniques address classification tasks such as image and video recognition. The Mini-EUSO telescope, operating onboard the International Space Station since 2019, collects ultraviolet atmospheric observations relevant to meteors and space debris. The work presents ML-based approaches for recognizing and classifying track-like signals in Mini-EUSO data, including signals whose light curves and kinematics resemble those expected from extensive air showers of ultra-high-energy cosmic rays. Results indicate that even simple neural networks achieve strong performance.","arXiv :2308 . 14948v1 [ astro-ph .IM] 29 Aug 2023  \nMachine Learning for Mini-EUSO Telescope Data Analysis  \nMario Bertaina, 􀀰,􀀱,∗ Mikhail Zotov, 􀀲 Dmitry Anzhiganov, 􀀳,􀀲 Dario Barghini, 􀀰,􀀱,􀀴 Carl Blaksley,􀀵 Antonio Giulio Coretti, 􀀰,􀀱 Aleksandr Kryazhenkov, 􀀳,􀀲 Antonio Montanaro􀀶 and Leonardo Olivi 􀀰 for the JEM-EUSO collaboration  \n􀀰 Dipartimento di Fisica, Università di Torino Torino, Italy  \n􀀱 INFN, Sezione di Torino Torino, Italy  \n􀀲 Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University Moscow, Russia  \n􀀳 Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University Moscow, Russia  \n􀀴 INAF, Osservatorio astrofisico di Torino Torino, Italy  \n􀀵 RIKEN Wako, Japan  \n􀀶 Department of Electronics and Telecommunications, Polytechnic University of Turin, Corso Duca degli Abruzzi 24, 10129 Turin, Italy  \nE-mail: [bertaina@to.infn.it](bertaina@to.infn.it)  \nNeural 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. MiniEUSO 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.  \n38th International Cosmic Ray Conference (ICRC2023)  \n26 July-3 August, 2023  \nNagoya, Japan  \n∗ Speaker  \n© Copyright owned by the author(s) under the terms of the Creative Commons  \nAttribution-NonCommercial-NoDerivatives 4 .0 International License (CC BY-NC-ND 4 .0) . [https://pos.sissa.it/](https://pos.sissa.it/)  \n1. Introduction  \nThe JEM-EUSO (Joint Exploratory Missions for Extreme Universe Space Observatory) collaboration is developing a program of studying ultra-high energy cosmic rays (UHECRs) with a wide angle telescope from a low Earth orbit [1] . The idea is based on the possibility to register fluorescence and Cherenkov radiation in the ultraviolet (UV) range that is emitted during development of extensive air showers generated by primary particles hitting the atmosphere.  \nIt was clear from the early stages of the development of the program that an orbital instrument of this kind can also collect information about other processes taking place in the atmosphere in the UV [2] . This was fully confirmed by the TUS mission, which took place on board the Lomonosov satellite in 2016–2017 [3] . These days, the Mini-EUSO telescope is being operated onboard International Space Station (ISS) as a part of an agreement between the Italian Space Agency (Agenzia Spaziale Italiana) and Roscosmos (Russia) with the aim of studying transient atmospheric phenomena, meteors, searching for interstellar meteors and strange quark matter, and mapping nocturnal emission of the atmosphere in the UV [4] . Here we briefly present results of applying neural networks and other machine learning methods aimed at recognition and reconstruction of track-like signals in the Mini-EUSO data. These are tracks of meteors and space debris as well as signals of short flashes with light curves and kinematics similar to those expected from extensive air showers (EAS) of extreme energies. We shall call the later signals as EAS-like.  \n2. Mini-EUSO Telescope  \nThe Mini-EUSO telescope is equipped with two Fresnel lenses with a diameter of 25 cm each, and a focal surface (FS) composed of 6 × 6 Hamamatsu R11265-M64 MAPMTs. Every MAPMT has 64 pixels, thus providing 2304 pixels in total. Each MAPMT has a BG3 UV-band glass filter. Mini-E","cbCaisVEK58kvCAr","https://ap.wps.com/l/cbCaisVEK58kvCAr","pdf",1378676,1,10,"English","en",105,"# Introduction\n## JEM-EUSO scientific motivation\n## Mini-EUSO observational goals\n# Mini-EUSO Telescope\n## Instrument layout and pixelization\n## Operating modes and time resolutions\n# Search for meteors and space debris\n## Meteor track characteristics","[{\"question\":\"What kinds of signals does the study focus on in Mini-EUSO data?\",\"answer\":\"The study targets track-like signals, including meteors and space debris, and also short-flash signals labeled as EAS-like, which mimic extensive air shower expectations in light curves and kinematics.\"},{\"question\":\"Why is machine learning useful for Mini-EUSO signal recognition?\",\"answer\":\"Machine learning methods are effective for classification problems, and the paper reports that even simple neural networks can achieve impressive performance for recognizing and classifying track-like signals.\"},{\"question\":\"How does Mini-EUSO operate in terms of data collection modes?\",\"answer\":\"Mini-EUSO collects data in three modes: D1 with 2.5 μs time resolution, D2 integrated over 128 D1 GTUs, and D3 integrated over 128×128 D1 GTUs, where D3 data are recorded as a stream during ISS nocturnal segments.\"}]","Machine Learning for Mini-EUSO Telescope Data Analysis | 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kinds of signals does the study focus on in Mini-EUSO data?","Question",{"text":75,"@type":76},"The study targets track-like signals, including meteors and space debris, and also short-flash signals labeled as EAS-like, which mimic extensive air shower expectations in light curves and kinematics.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is machine learning useful for Mini-EUSO signal recognition?",{"text":80,"@type":76},"Machine learning methods are effective for classification problems, and the paper reports that even simple neural networks can achieve impressive performance for recognizing and classifying track-like signals.",{"name":82,"@type":73,"acceptedAnswer":83},"How does Mini-EUSO operate in terms of data collection modes?",{"text":84,"@type":76},"Mini-EUSO collects data in three modes: D1 with 2.5 μs time resolution, D2 integrated over 128 D1 GTUs, and D3 integrated over 128×128 D1 GTUs, where D3 data are recorded as a stream during ISS nocturnal 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