[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126758-en":3,"doc-seo-126758-105":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},126758,962084928432,"Emma Wilson","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Lessons Learned from the 1st ARIEL Machine Learning Challenge - Correcting Transiting Exoplanet Light Curves for Stellar Spots","The last decade has accelerated exoplanet discovery and characterisation, yet key obstacles remain, many addressable through machine learning. Transit photometry, a central technique for detecting planets and inferring their properties, is highly sensitive to stellar spots that distort transit depth measurements. The work targets automated, efficient, and accurate derivation of transit depths from spot-contaminated light curves. It presents a comprehensive set of methods, describes Ariel-like simulated data and challenge design, and reports top-5 team solutions, including nonlinear modeling with deep networks and ensembles or linear-statistics approaches with strong predictive performance.","# Lessons Learned from the 1st ARIEL Machine Learning Challenge:Correcting Transiting Exoplanet Light Curves for Stellar Spots\n\nNikolaos Nikolaou,*Ingo P.Waldmann,Angelos Tsiaras,2,1 Mario Morvan,Billy Edwards,Kai Hou Yip,¹Alexandra Thompson,¹Giovanna Tinetti,¹Subhajit Sarkar,3James M.Dawson,³Vadim Borisov,⁴Gjergji Kasneci,4 Matej Petkovic,5 Tomaž Stepišnik,5 Tarek Al-Ubaidi,6,7Rachel Louise Bailey,?Michael Granitzer,8 Sahib Julka,8Roman Kern,9 Patrick Ofner,9Stefan Wagner,10 Lukas Heppe,¹Mirko Bunse,¹Katharina Morik¹¹and Luís F.Simoes¹2  \nDepartment of Physics and Astronomy,University College London,Gower Street,London,WCIE6BT,UK2INAF—Osservatorio Astrofisico diArcetri,Largo E.Fermi 5,I-50125 Firenze,Italy3School ofPhysics and Astronomy,Cardif University,The Parade,Cardif,CF243AA,UK4Department of Computer Science,University of Tuebingen,Tuebingen,Germany5Jožef Stefan Institute,Ljubljana,Slovenia6DCCS GmbH,Austria  \n⁷Space Research Institute,Austrian Academy of Sciences,Austria8Chair of Data Science,University of Passau,Germany  \n9Know-Center GmbH,Research Center for Data-Driven Business &Big Data Analytics,Austria10Commission for Astronomy,Austrian Academy of Sciences,Graz,Austria  \nllLamarr hnstitute for Machine Leaming and Arifcial Inteligence,TU Dortmund University,Germany12MLAnalytics,Lisbon,Portugal  \nAccepted XXX.Received YYY;in original form ZZZ  \n## ABSTRACT\n\nThe last decade has witnessed a rapid growth of the field of exoplanet discovery and characterisation.However,several bigchallenges remain,many of which could be addressed using machine learning methodology.For instance,the most prolificmethod for detecting exoplanets and inferring several of their characteristics,transit photometry,is very sensitive to the presenceof stellar spots.The current practice in the literature is to identify the effects of spots visually and correct for them manually ordiscard the affected data.This paper explores a first step towards fully automating the efficient and precise derivation of transitdepthsfrom transit light curves in the presence of stellar spots.The primary focus of the paper is to present in detail a diversearsenal of methods for doing so.The methods and results we present were obtained in the context of the 1st Machine LearningChallenge organized for the European Space Agency's upcoming Ariel mission.We first present the problem,the simulatedAriel-like data and outline the Challenge while identifying best practices for organizing similar challenges in the future.Finally,we present the solutions obtained by the top-5 wining teams,provide their code and discuss their implications.Successfulsolutions either construct highly non-linear(wnt,the raw data)models with minimal preprocessing -deep neural networks andensemble methods-or amount to obtaining meaningful statistics from the light curves,constructing linear models on whichyields comparably good predictive performance.  \nKey words:machine learning-data methods-exoplanets-transit photometry-light curves-stellar spots  \n## 1 INTRODUCTION\n\nIn the coming decade,exoplanet atmospheric spectroscopy will un-dergo a revolution with a number of upcoming space and ground-based instruments providing unprecedented amounts of high-qualitydata.Most notable are of course the Extremely Large Telescopes(e.g.Gilmozzi &Spyromilio 2007;Nelson &Sanders 2008;Johnset al.2012)on the ground and the James Webb Space Telescope(Gardner et al.2006)and the Ariel space telescope(Tinetti et al.  \n*E-mail:n.nikolaou@ucl.ac.uk  \nDownloaded from https:/academic.oup.com/rastiladvance-article/doi/10.1093/rastirzad050/7336982 by guest on 15 November 2023  \n2016a).One of the outstanding challenges to high-precision spec-trophotometry of exoplanets is the presence of stellar noise.Herewe will address in particular the presence of occulted star spotsin the spectro-photometric light curves of the Ariel space mission.The chromatic dependence of spots and faculae can adversely affectthe measured exoplanetary tran","cbCaifVnX9DRkaiX","https://ap.wps.com/l/cbCaifVnX9DRkaiX","pdf",2972318,1,17,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"Why are stellar spots a problem for transit photometry?\",\"answer\":\"Stellar spots change the observed light curve during transits, biasing the derived transit depth and affecting other parameters such as limb-darkening and mid-transit times.\"},{\"question\":\"What is the main goal of the paper?\",\"answer\":\"To take a first step toward fully automating the efficient and precise correction of transit depth derivation from transit light curves that include stellar spot effects.\"},{\"question\":\"How was the Ariel mission challenge used in this study?\",\"answer\":\"The methods and results were developed using simulated Ariel-like data, and the paper outlines the Challenge setup, then summarizes solutions from the top-5 winning teams and their implications.\"}]","Lessons Learned from the 1st ARIEL Machine Learning Challenge - Correcting Transiting Exoplanet Light Curves for Stellar Spots | PDF",1785934623,43,{"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},"lessons-learned-from-the-1st-ariel-machine-learning-challenge-correcting-transiting-exoplanet-light-curves-for-stellar-spots-126758","",{"@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/lessons-learned-from-the-1st-ariel-machine-learning-challenge-correcting-transiting-exoplanet-light-curves-for-stellar-spots-126758/126758/",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","2026-08-05",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},"Why are stellar spots a problem for transit photometry?","Question",{"text":75,"@type":76},"Stellar spots change the observed light curve during transits, biasing the derived transit depth and affecting other parameters such as limb-darkening and mid-transit times.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the main goal of the paper?",{"text":80,"@type":76},"To take a first step toward fully automating the efficient and precise correction of transit depth derivation from transit light curves that include stellar spot effects.",{"name":82,"@type":73,"acceptedAnswer":83},"How was the Ariel mission challenge used in this study?",{"text":84,"@type":76},"The methods and results were developed using simulated Ariel-like data, and the paper outlines the Challenge setup, then summarizes solutions from the top-5 winning teams and their 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