[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118099-en":3,"doc-seo-118099-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},118099,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Asteroids co-orbital motion classification based on Machine Learning","This work classifies asteroids in co-orbital motion with a given planet using Machine Learning, focusing on four resonance motion types: Tadpole at L4 and L5, Horseshoe, and Quasi-Satellite. Three datasets are built for training and testing—Real from JPL Horizons ephemerides, plus Ideal and Perturbed generated by propagating initial conditions under two dynamical systems. A tailored pipeline extracts time-series features from the resonance-related angle θ via TSFRESH, then applies dimensionality reduction and classification. The feature-based method supports smaller datasets than deep learning, enables feature-importance ranking, and improves interpretability through SHAP explainability.","MNRAS 527, 6439–6454 (2024) [https://doi.org/10.1093/mnras/stad3603](https://doi.org/10.1093/mnras/stad3603)  \nAdvance Access publication 2023 November 22  \nAsteroids co-orbital motion classification based on Machine Learning  \nGiulia Ciacci,1 Andrea Barucci  , 1‹ Sara Di Ruzza2 and Elisa Maria Alessi3  \n1IFAC-CNR, Istituto di Fisica Applicata ‘Nello Carrara’, Consiglio Nazionale delle Ricerche, via Madonna del Piano 10, I-50019 Sesto Fiorentino (FI), Italy  \n2Dipartimento di Matematica e Informatica, Universit`a di Palermo, Via Archirafi 34, I-90123 Palermo, Italy  \n3IMATI-CNR, Istituto di Matematica Applicata e Tecnologie informatiche ‘E. Magenes’, Consiglio Nazionale delle Ricerche, Via Alfonso Corti 12, I-20133 Milano, Italy  \nAccepted 2023 November 15. Received 2023 October 25; in original form 2023 September 15  \nABSTRACT  \nIn this work, we explore how to classify asteroids in co-orbital motion with a given planet using Machine Learning. We consider four different kinds of motion in mean motion resonance with the planet, nominally Tadpole at L4 and L5 , Horseshoe and Quasi-Satellite, building three data sets defined as Real (taking the ephemerides of real asteroids from the JPL Horizons system), Ideal and Perturbed (both simulated, obtained by propagating initial conditions considering two different dynamical systems) for training and testing the Machine Learning algorithms in different conditions. The time series of the variable θ (angle related to the resonance) are studied with a data analysis pipeline defined ad hoc for the problem and composed by: data creation and annotation, time series features extraction thanks to the TSFRESH package (potentially followed by selection and standardization) and the application of Machine Learning algorithms for Dimensionality Reduction and Classification. Such approach, based on features extracted from the time series, allows to work with a smaller number of data with respect to Deep Learning algorithms, also allowing to define a ranking of the importance of the features. Physical interpretability of the features is another key point of this approach. In addition, we introduce the SHapley Additive exPlanations for Explainability technique. Different training and test sets are used, in order to understand the power and the limits of our approach. The results show how the algorithms are able to identify and classify correctly the time series, with a high degree of performance.  \nKey words: methods: numerical–celestial mechanics–minor planets, asteroids: general.  \n1 INTRODUCTION  \nIn the last decades, the use of Artificial Intelligence (AI) for data analysis has significantly increased in scientific applications, in particular thanks to its sub-field known as Machine Learning (ML), where an algorithm is said to improve its performance on a specific task by experience (e.g. Hastie et al. 2009b; Jordan & Mitchell 2015) . More recently, many authors started to use such methods in astronomy and Solar system science (e.g. Ball & Brunner 2010; Ivezi et al. 2014) . Although well-known and broadly applied in several contexts, we recall here the general concepts of AI and ML, for the sake of completeness. With AI we mean methods by which a computer makes decisions or discoveries that would usually require human intelligence, while with ML we mean automated processes that learn by examples in order to classify, predict, discover or generate new data. Part of ML is the class of algorithms known as Deep Learning (DL) which is based on artificial neural networks (e.g. LeCun, Bengio & Hinton 2015; Goodfellow, Bengio & Courville 2016). ML and DL are the key of the success of AI nowadays. There are three classes of ML algorithms (see e.g. Hastie, Tibshirani & Friedman 2009a for more details): supervised learning, where a labelled data set is used to help to train and tune the algorithm, with  \n􀀃 E-mail: [a.barucci@ifac.cnr.it](a.barucci@ifac.cnr.it)  \nthe goal to create a map that links inputs to outputs; unsu","cbCait1oNC0NoYFd","https://ap.wps.com/l/cbCait1oNC0NoYFd","pdf",2203287,1,16,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What asteroid co-orbital motion types are classified in this study?\",\"answer\":\"The work classifies Tadpole motion at L4 and L5, Horseshoe motion, and Quasi-Satellite motion in mean motion resonance with a planet.\"},{\"question\":\"How are the training and testing datasets constructed?\",\"answer\":\"Three datasets are used: Real based on JPL Horizons ephemerides of real asteroids, and Ideal and Perturbed generated by simulating two dynamical systems from propagated initial conditions.\"},{\"question\":\"What analysis pipeline is used to process the resonance angle time series?\",\"answer\":\"The resonance-related angle θ time series are processed by a tailored pipeline including data creation and annotation, feature extraction with TSFRESH, and then dimensionality reduction and machine learning classification.\"}]","Asteroids co-orbital motion classification based on Machine Learning | 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asteroid co-orbital motion types are classified in this study?","Question",{"text":76,"@type":77},"The work classifies Tadpole motion at L4 and L5, Horseshoe motion, and Quasi-Satellite motion in mean motion resonance with a planet.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How are the training and testing datasets constructed?",{"text":81,"@type":77},"Three datasets are used: Real based on JPL Horizons ephemerides of real asteroids, and Ideal and Perturbed generated by simulating two dynamical systems from propagated initial conditions.",{"name":83,"@type":74,"acceptedAnswer":84},"What analysis pipeline is used to process the resonance angle time series?",{"text":85,"@type":77},"The resonance-related angle θ time series are processed by a tailored pipeline including data creation and annotation, feature extraction with TSFRESH, and then dimensionality reduction and machine learning 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