[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124015-en":3,"doc-seo-124015-105":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},124015,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Detecting and Classifying Flares in High-Resolution Solar Spectra - with Supervised Machine Learning","Solar flares are tied to the Sun’s magnetic activity and can strongly affect exoplanet transmission spectroscopy through stellar spectral contamination. A standardized workflow is presented to detect and categorize solar flares using supervised machine learning, trained on RHESSI flare data together with HARPS-N high-resolution solar spectra. The best model is a C-Support Vector Machine using non-linear radial basis function kernels, achieving aggregate accuracy around 0.65 and class accuracy above 0.70 for no-flare and weak-flare categories. Tests confirm generalization to new data with different flare characteristics and distributions, with future work targeting higher accuracy, alternative model evaluation, and expanded datasets.","arXiv :2406 . 15594v1 [ astro-ph . SR] 21 Jun 2024  \nDraft version June 25, 2024  \nTypeset using LATEX twocolumn style in AASTeX631  \nDetecting and Classifying Flares in High-Resolution Solar Spectra  \nwith Supervised Machine Learning  \nNicole Hao  ,1 Laura Flagg  ,2, 1 and Ray Jayawardhana 2  \n1 Cornell University, Ithaca, NY 14853, USA  \n2 Department of Physics and Astronomy, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA  \nABSTRACT  \nFlares are a well-studied aspect of the Sun’s magnetic activity. Detecting and classifying solar flares can inform the analysis of contamination caused by stellar flares in exoplanet transmission spectra. In this paper, we present a standardized procedure to classify solar flares with the aid of supervised machine learning. Using flare data from the RHESSI mission and solar spectra from the HARPS-N instrument, we trained several supervised machine learning models, and found that the best performing algorithm is a C-Support Vector Machine (SVC) with non-linear kernels, specifically Radial Basis Functions (RBF) . The best-trained model, SVC with RBF kernels, achieves an average aggregate accuracy score of 0.65, and categorical accuracy scores of over 0.70 for the no-flare and weak-flare classes, respectively. In comparison, a blind classification algorithm would have an accuracy score of 0.33 . Testing showed that the model is able to detect and classify solar flares in entirely new data with different characteristics and distributions from those of the training set. Future efforts could focus on enhancing classification accuracy, investigating the efficacy of alternative models, particularly deep learning models, and incorporating more datasets to extend the application of this framework to stars that host exoplanets.  \n1. INTRODUCTION  \nTransmission spectroscopy is highly useful and widely used for characterizing exoplanets since it can yield valuable constraints on the nature and composition of planetary atmospheres. Yet, due to the inhomogeneity and time variability of the stellar photo-and chromospheres, this method is intrinsically impacted by stellar spectral contamination (Rackham et al. 2023) . Often, the stellar contamination will rival or even exceed the planetary spectral features, making it very difficult to disentangle the exoplanet atmospheric signals from stellar contamination (Rackham et al. 2023) .  \nSuch contamination poses a challenge for measuring an exoplanet’s transit depth accurately. To consider the impact of stellar flares, it is helpful to begin with an investigation of solar flares, given the abundance of flare data for the Sun. Efficient detection and classification methods for solar flares in transmission spectra  \nCorresponding author: Laura Flagg  \n[laura.s.flagg@gmail.com](laura.s.flagg@gmail.com)  \ncould help astronomers correct for stellar contamination in exoplanet transmission spectra with greater accuracy. This study has been designed with two primary objectives. First, it aims to address the challenge of detecting flare events in high-resolution solar spectra. To achieve this, we correlated solar spectra with solar flare events based on their start and end times, labeled solar spectra, and fed labeled solar spectra into supervised machine-learning models, which enabled accurate detection of flares based on their energy levels. Second, the project strives to develop a robust machine-learning model that can classify flares in solar spectra as accurately as possible. Identifying low-energy flares in highresolution solar spectra may present a greater difficulty compared to their high-energy counterparts. However, the impact of low-energy flares on spectra should not be disregarded. Thus, we aim to detect and classify all flares in solar spectra, regardless of their energy levels. In this study, we employed supervised learning algorithms, specifically Support Vector Classification (SVC), to detect and classify solar flares in high-reso","cbCaikKZVlI5qg6X","https://ap.wps.com/l/cbCaikKZVlI5qg6X","pdf",2368848,1,9,"English","en",105,"# Introduction\n## Objectives and methodology overview\n# Data\n## HARPS-N solar spectra (2015–2018)\n## RHESSI flare observations\n# Methods and model evaluation (described in text)","[{\"question\":\"Why are solar flares important for exoplanet transmission spectroscopy?\",\"answer\":\"Solar flares contribute stellar spectral contamination that can rival or exceed planetary atmospheric signatures, making it hard to separate exoplanet signals. Studying solar flares helps improve corrections for this contamination.\"},{\"question\":\"What machine-learning approach performs best in the study?\",\"answer\":\"A C-Support Vector Machine with non-linear radial basis function (RBF) kernels yields the strongest results. It reaches average aggregate accuracy of about 0.65 and categorical accuracy above 0.70 for the no-flare and weak-flare classes.\"},{\"question\":\"How does the model handle new data not seen during training?\",\"answer\":\"Testing shows the trained model can detect and classify solar flares in entirely new datasets with different characteristics and distributions than the training set.\"}]","Detecting and Classifying Flares in High-Resolution Solar Spectra - with Supervised Machine Learning | PDF",1785819855,23,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"detecting-and-classifying-flares-in-high-resolution-solar-spectra-with-supervised-machine-learning","",{"@graph":36,"@context":86},[37,54,69],{"@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/detecting-and-classifying-flares-in-high-resolution-solar-spectra-with-supervised-machine-learning/124015/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why are solar flares important for exoplanet transmission spectroscopy?","Question",{"text":76,"@type":77},"Solar flares contribute stellar spectral contamination that can rival or exceed planetary atmospheric signatures, making it hard to separate exoplanet signals. Studying solar flares helps improve corrections for this contamination.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What machine-learning approach performs best in the study?",{"text":81,"@type":77},"A C-Support Vector Machine with non-linear radial basis function (RBF) kernels yields the strongest results. It reaches average aggregate accuracy of about 0.65 and categorical accuracy above 0.70 for the no-flare and weak-flare classes.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the model handle new data not seen during training?",{"text":85,"@type":77},"Testing shows the trained model can detect and classify solar flares in entirely new datasets with different characteristics and distributions than the training set.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},"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":107,"slug":138},19,"General","general"]