[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118553-en":3,"doc-seo-118553-105":30,"detail-sidebar-cat-0-en-105":90},{"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},118553,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Harnessing HAVOK and Machine Learning for Cosmic Ray Forecasting","This research develops an innovative forecasting approach for cosmic ray activity by combining the HAVOK model with machine learning. Using data from the Tbilisi Cosmic Rays Observatory from 2012 to 2020, it analyzes chaotic dynamics through Lorenz and Mackey-Glass models to validate the HAVOK-ML framework for predicting cosmic ray flux. Results indicate strong potential to improve space weather forecasts, with wider relevance to solar activity, supernovae, gamma-ray bursts, and air quality monitoring. The implementation uses Python, Julia, and Keras-based ML tools.","HARNESSING HAVOK AND MACHINE LEARNING FOR COSMIC RAY FORECASTING  \n*Takadze G., **Larry D., **Wascak J.  \n*Mikheil Nodia Institute of Geophysics of Ivane Javakhishvili Tbilisi State University, Tbilisi, Georgia  \n**University Texas at Dallas  \nAbstract. This research explores an innovative approach to forecasting cosmic ray activity by integrating the HAVOK model with machine learning algorithms. The study uses data from the Tbilisi Cosmic Rays Observatory, spanning from 2012 to 2020. The focus is on analyzing chaotic systems, including the Lorenz and Mackey-Glass models, to validate the HAVOK-ML method for predicting cosmic ray flux. The results demonstrate the method’s potential to enhance space weather forecasts, with broader applications in predicting solar activity, supernovae, gamma-ray bursts, and air quality monitoring. The model is implemented using Python, Julia, and machine learning frameworks like Keras.  \nKeywords: HAVOK model, Space weather, Lorenz system, Mackey-Glass system, Chaos theory, Time series prediction, Air quality, Solar activity, Supernovae, Python, Julia, Keras.  \nIntroduction  \nCosmic rays, high-energy atomic nuclei that travel through space at nearly the speed of light, provide crucial insights into various cosmic phenomena such as supermassive black holes, exploding stars, and the chemical and physical makeup of the universe. These rays, predominantly originating from supernova explosions, interact with Earth's atmosphere, creating secondary particles that can be detected and analyzed. Understanding and predicting the behavior of cosmic rays is essential for advancing our knowledge of space weather and its impact on technology and communications. This research aims to improve cosmic ray activity forecasts by combining the HAVOK model with machine learning algorithms. The HAVOK model is a data-driven method that decomposes complex time series data into simpler, interpretable components, while machine learning learns from historical data, identifies patterns, and improves forecast accuracy. Together, these approaches provide a powerful tool for analyzing chaotic systems and enhancing the prediction of cosmic ray behavior. The HAVOK-ML method employs the Singular Value Decomposition (SVD) of Hankel matrices to identify dominant modes within a system, constructing a low-dimensional model that simplifies the analysis of future states. By applying this method to well-known chaotic systems such as the Lorenz system, the study demonstrates its effectiveness in predicting cosmic ray activity. The data used in this research, sourced from the Tbilisi Cosmic Rays Observatory, spans from 2012 to 2020 and includes cosmic ray interactions captured by a 9-channel detector. These data, combined with advanced programming techniques using Python, Julia, and machine learning frameworks such as Keras, are instrumental in the implementation of the HAVOK-ML method  \nThe purpose of task  \nThe purpose of this research is to develop an enhanced method for predicting cosmic ray activity by combining the HAVOK model with machine learning algorithms. This approach aims to improve the accuracy of forecasts related to space weather phenomena, particularly the impact of cosmic ray flux on Earth’s magnetic field and atmosphere. As cosmic rays interact with Earth's magnetic field, they are often deflected, though higher-energy cosmic rays can penetrate near the magnetic poles, particularly during periods of geomagnetic storms caused by solar flares and coronal mass ejections (CMEs) . Understanding  \nthese interactions is crucial, as fluctuations in Earth's magnetic field over time can influence cosmic ray penetration, impacting atmospheric chemistry, cloud formation, and climate. By analyzing chaotic systems and utilizing data from the Tbilisi Cosmic Rays Observatory, this study seeks to demonstrate the effectiveness of the HAVOK-ML method, with broader applications in predicting solar activity, supernovae, geomagnetic field reve","cbCairu9mLttsr8i","https://ap.wps.com/l/cbCairu9mLttsr8i","pdf",516332,1,4,"English","en",105,"# Introduction\n# The purpose of task\n# Results\n## Lorenz System Training Data\n## Mackey-Glass Chaotic Series Training Data\n## Cosmic Ray Training Data\n## HAVOK-ML Prediction of Lorenz Time Series using Cosmic Rays","[{\"question\":\"What forecasting method does the study propose for cosmic ray activity?\",\"answer\":\"The study proposes a hybrid HAVOK-ML approach that integrates the HAVOK model with machine learning algorithms for time series prediction of cosmic ray activity.\"},{\"question\":\"What data is used to validate the HAVOK-ML method?\",\"answer\":\"Validation uses Tbilisi Cosmic Rays Observatory data spanning 2012 to 2020, together with well-known chaotic systems represented by the Lorenz and Mackey-Glass models.\"},{\"question\":\"Which systems and outcomes demonstrate the effectiveness of the method?\",\"answer\":\"The method is demonstrated on Lorenz and Mackey-Glass time series, and on cosmic ray training data, with predictions that align closely with actual observations.\"}]","Harnessing HAVOK and Machine Learning for Cosmic Ray Forecasting | PDF",1785684125,10,{"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":85,"head_meta":87,"extra_data":89,"updated_unix":28},"harnessing-havok-and-machine-learning-for-cosmic-ray-forecasting","",{"@graph":36,"@context":84},[37,53,67],{"@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":21},"https://docshare.wps.com/document/harnessing-havok-and-machine-learning-for-cosmic-ray-forecasting/118553/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What forecasting method does the study propose for cosmic ray activity?","Question",{"text":74,"@type":75},"The study proposes a hybrid HAVOK-ML approach that integrates the HAVOK model with machine learning algorithms for time series prediction of cosmic ray activity.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What data is used to validate the HAVOK-ML method?",{"text":79,"@type":75},"Validation uses Tbilisi Cosmic Rays Observatory data spanning 2012 to 2020, together with well-known chaotic systems represented by the Lorenz and Mackey-Glass models.",{"name":81,"@type":72,"acceptedAnswer":82},"Which systems and outcomes demonstrate the effectiveness of the method?",{"text":83,"@type":75},"The method is demonstrated on Lorenz and Mackey-Glass time series, and on cosmic ray training data, with predictions that align closely with actual observations.","https://schema.org",{"og:url":52,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":29,"slug":132},"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]