[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120672-en":3,"doc-seo-120672-105":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},120672,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Solar Wind Speed Estimate - Solar Wind Speed Estimate with Machine Learning Ensemble Models for LISA","This work investigates how machine learning ensemble models can reconstruct solar wind speed observations from the first Lagrangian point measured by ACE during 2016–2017, using galactic cosmic-ray flux variations from particle detectors on the LISA Pathfinder mission. Results show that ensembles built from heterogeneous weak regressors achieve higher predictive accuracy than single weak regressors. The study highlights ML’s potential as a software surrogate for diagnostics in space missions, enabling solar wind monitoring and space weather inference without dedicated solar-wind-speed instrumentation.","Solar Wind Speed Estimate with Machine Learning Ensemble Models for LISA  \nFederico Sabbatini\u003C , Catia Grimani  \nDepartment of Pure and Applied Sciences, University of Urbino Carlo Bo, Via S. Chiara, 27, Urbino, 61029, Italy Section in Florence, INFN, Via B. Rossi, Sesto Fiorentino, 50019, Florence, Italy  \n\n| ARTICLE INFO |  | AB STRACT |\n| --- | --- | --- |\n| Keywords:\u003Cbr>LISA Pathfinder Galactic cosmic rays Solar wind Ensemble regressor |  | In this work we study the potentialities of machine learning models in reconstructing the solar wind speed observations gathered in the first Lagrangian point by the ACE satellite in 2016–2017 using as input data galactic cosmic-ray flux variations measured with particle detectors hosted onboard the LISA Pathfinder mission also orbiting around L1 during the same years. We show that ensemble models composed of heterogeneous weak regressors are able to outperform weak regressors in terms of predictive accuracy. Machine learning and other powerful predictive algorithms open a window on the possibility of substituting dedicated instrumentation with software models acting as surrogates for diagnostics of space missions such as LISA and space weather science. |\n\n1. Introduction  \nRecent years have seen an exponential increase in the application of machine learning (ML) techniques to several fields, including physics and astrophysics (Nguyen et al., 2019; Zhou et al., 2021; Reiss et al., 2021; Rüdisser et al., 2022) . ML-based tools provide predictions more accurate than other models due to their generalisation properties. Furthermore, the current availability of computational power makes feasible building complex and prediction-effective predictors. These are the main reasons behind the heavy application of ML algorithms, even though they usually require a huge amount of training data to be provided. This need of large training data sets may be challenging in some scenarios, but it does not constitute a limitation when ML predictors rely on observations gathered on beam experiments in high-energy physics and by long-lasting space missions, for instance.  \nPredictive tools in general, and ML models in particular, are precious resources for space missions to achieve several goals, as time series missing data filling and pattern recognition (Villani et al., 2022) . Even in the case that some data are not available at all, ML predictors may allow to provide these data. The future European Space Agency (ESA) Laser Interferometer Space Antenna (LISA; Amaro-Seoane et al., 2017) mission, devoted to the detection of low-frequency gravitational waves in space, will host magnetometers and radiation monitors for diagnostics (Cesarini et al., 2022) . However, no instruments dedicated to the measure of solar wind speed will be placed onboard LISA (Armano et al., 2018a, 2019; Villani et al., 2022; Cesarini et al., 2022) . We show here that a predictive ML model may allow us to estimate the solar wind speed variations for LISA by using, as input data, galactic cosmic-ray (GCR) flux observations that appear modulated at the transit of high-speed solar wind streams. We have developed a ML predictive model for LISA based on the LISA Pathfinder (LPF; Armano et al., 2009; Antonucci et al., 2011, 2012; Armano et al., 2016, 2018b; Grimani et al., 2022) data gathered between February, 2016 and July, 2017 around the L1 Lagrangian point. The outcomes of the model are compared to observations gathered contemporaneously by the NASA mission ACE (Stone et al., 2013) dedicated to interplanetary medium parameter monitoring, also orbiting around L1 during the same period of time of LPF. It is worthwhile to point out that ACE observations can be used also for LPF because the distance between the two spacecraft was always smaller than the solar wind correlation length (Wicks et al., 2010) . In this paper we show that GCR flux short-term recurrent variations allow to reconstruct the solar wind speed trend  \narXiv :2302 .06740v1 [ astro-p","cbCaifMhrkvxJcpD","https://ap.wps.com/l/cbCaifMhrkvxJcpD","pdf",1712714,1,15,"English","en",105,"# Introduction\n## Motivation and problem setting\n## Role of ML for space missions\n# Background and Motivations\n## The LISA and LISA Pathfinder missions\n# Ensemble model\n## Data preprocessing\n## Model tuning and testing\n# Conclusions","[{\"question\":\"What inputs are used to estimate solar wind speed for LISA?\",\"answer\":\"The model uses galactic cosmic-ray (GCR) flux variations measured by particle detectors on the LISA Pathfinder mission, which exhibit modulation when high-speed solar wind streams pass.\"},{\"question\":\"Why do ensemble models improve predictive accuracy?\",\"answer\":\"Ensemble models combine heterogeneous weak regressors, and the study shows they outperform individual weak regressors by providing better generalization and accuracy.\"},{\"question\":\"How are the model results validated?\",\"answer\":\"Predictions are compared against solar wind speed observations contemporaneously collected by NASA’s ACE mission, which monitors interplanetary medium parameters while orbiting around L1.\"}]","Solar Wind Speed Estimate - Solar Wind Speed Estimate with Machine Learning Ensemble Models for LISA | PDF",1785731273,38,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"solar-wind-speed-estimate-solar-wind-speed-estimate-with-machine-learning-ensemble-models-for-lisa","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/solar-wind-speed-estimate-solar-wind-speed-estimate-with-machine-learning-ensemble-models-for-lisa/120672/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What inputs are used to estimate solar wind speed for LISA?","Question",{"text":75,"@type":76},"The model uses galactic cosmic-ray (GCR) flux variations measured by particle detectors on the LISA Pathfinder mission, which exhibit modulation when high-speed solar wind streams pass.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why do ensemble models improve predictive accuracy?",{"text":80,"@type":76},"Ensemble models combine heterogeneous weak regressors, and the study shows they outperform individual weak regressors by providing better generalization and accuracy.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the model results validated?",{"text":84,"@type":76},"Predictions are compared against solar wind speed observations contemporaneously collected by NASA’s ACE mission, which monitors interplanetary medium parameters while orbiting around L1.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]