[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118934-en":3,"doc-seo-118934-105":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},118934,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","From Candidate Identification to Planet Characterization - A Machine Learning Approach","This PhD thesis develops a transferable machine-learning framework to search for and characterize planets from the WASP survey. Two methods—a Random Forest Classifier and a Convolutional Neural Network—are used to identify new exoplanet candidates from archival data and lightcurves, supported by a standardized catalog of 1,041 verified false positives. The thesis then introduces a stacking framework that improves performance by combining predictions from multiple models, enabling ranking of 100,000 unlabeled lightcurves. Finally, a new MCMC method integrates transit and radial-velocity likelihoods with priors from spectroscopy and Gaia parallax to constrain stellar parameters and characterize two confirmed hot Jupiters.","From candidate identification to planet characterization:  \na machine learning approach  \nNicole Schanche  \nA thesis submitted for the degree of PhD  \nat the  \nUniversity of St Andrews  \n2020  \nFull metadata for this thesis is available in St Andrews Research Repository at:  \n[https://research-repository.st-andrews.ac.uk/](https://research-repository.st-andrews.ac.uk/)  \n[Identifier to use to cite or link to this thesis:](Identifier to use to cite or link to this thesis:)[ ](Identifier to use to cite or link to this thesis:)DOI: [https://doi.org/10.17630/sta/784](https://doi.org/10.17630/sta/784)  \nThis item is protected by original copyright  \nCandidate's declaration  \nI, Nicole Schanche, do hereby certify that this thesis, submitted for the degree of PhD, which is approximately 27,500 words in length, has been written by me, and that it is the record of work carried out by me, or principally by myself in collaboration with others as acknowledged, and that it has not been submitted in any previous application for any degree.  \nI was admitted as a research student at the University of St Andrews in September 2016.  \nI received funding from an organisation or institution and have acknowledged the funder(s) in the full text of my thesis.  \nDate Signature of candidate  \n29-Sept-2020  \nSupervisor's declaration  \nI hereby certify that the candidate has fulfilled the conditions of the Resolution and Regulations appropriate for the degree of PhD in the University of St Andrews and that the candidate is qualified to submit this thesis in application for that degree.  \nDate Signature of supervisor  \n29-Sept-2020  \nPermission for publication  \nIn submitting this thesis to the University of St Andrews we understand that we are giving permission for it to be made available for use in accordance with the regulations of the University Library for the time being in force, subject to any copyright vested in the work not being affected thereby. We also understand, unless exempt by an award of an embargo as requested below, that the title and the abstract will be published, and that a copy of the work may be made and supplied to any bona fide library or research worker, that this thesis will be electronically accessible for personal or research use and that the library has the right to migrate this thesis into new electronic forms as required to ensure continued access to the thesis.  \nI, Nicole Schanche, confirm that my thesis does not contain any third-party material that requires copyright clearance.  \nThe following is an agreed request by candidate and supervisor regarding the publication of this thesis:  \nPrinted copy  \nNo embargo on print copy.  \nElectronic copy  \nNo embargo on electronic copy.  \nDate Signature of candidate  \n29-Sept-2020  \nDate Signature of supervisor  \n29-Sept-2020  \nUnderpinning Research Data or Digital Outputs  \nCandidate's declaration  \nI, Nicole Schanche, hereby certify that no requirements to deposit original research data or digital outputs apply to this thesis and that, where appropriate, secondary data used have been referenced in the full text of my thesis.  \nDate Signature of candidate  \n29-Sept-2020  \nAbstract  \nFun will now commence.  \n-7 of 9, Star Trek VOY: Ashes to Ashes  \nThis thesis is broken into three main sections tracing the steps of the development of a new framework to search for and characterize planets from the WASP survey. While all methods were developed specifically for the WASP project, the principles are easily transferable to any ground or space based survey. In the first part of the thesis, I discuss the development of two machine learning methods, a Random Forest Classifier anda Convolutional Neural Network, that are able to find new exoplanet candidates from WASP archival data and lightcurves. In preparing the training dataset, I also created a standardized catalog of 1,041 false positives from SuperWASP, the northern component of WASP, that were verified with additional observations from ot","cbCaidXRZLThyFPH","https://ap.wps.com/l/cbCaidXRZLThyFPH","pdf",15312205,1,140,"English","en",105,"# Abstract\n## Candidate identification using machine learning\n## Stacking framework for improved prediction\n## Planet characterization with MCMC and multi-source priors","[{\"question\":\"What machine learning methods are developed to identify exoplanet candidates?\",\"answer\":\"The thesis develops a Random Forest Classifier and a Convolutional Neural Network to find new exoplanet candidates from WASP archival data and lightcurves.\"},{\"question\":\"How does the stacking framework improve candidate ranking?\",\"answer\":\"It uses predictions from multiple machine learning methods as inputs to a second-level classifier, providing improved performance over any single classifier and enabling ranking of over 100,000 lightcurves.\"},{\"question\":\"How are confirmed planets characterized in the thesis?\",\"answer\":\"A new MCMC method combines transit and radial-velocity likelihood fits with prior knowledge from optical/infrared spectrophotometry and Gaia parallax to constrain stellar parameters, then applies it to characterize two confirmed hot Jupiters.\"}]","From Candidate Identification to Planet Characterization - A Machine Learning Approach | PDF",1785721056,353,{"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},"from-candidate-identification-to-planet-characterization-a-machine-learning-approach","",{"@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/from-candidate-identification-to-planet-characterization-a-machine-learning-approach/118934/",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 machine learning methods are developed to identify exoplanet candidates?","Question",{"text":75,"@type":76},"The thesis develops a Random Forest Classifier and a Convolutional Neural Network to find new exoplanet candidates from WASP archival data and lightcurves.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the stacking framework improve candidate ranking?",{"text":80,"@type":76},"It uses predictions from multiple machine learning methods as inputs to a second-level classifier, providing improved performance over any single classifier and enabling ranking of over 100,000 lightcurves.",{"name":82,"@type":73,"acceptedAnswer":83},"How are confirmed planets characterized in the thesis?",{"text":84,"@type":76},"A new MCMC method combines transit and radial-velocity likelihood fits with prior knowledge from optical/infrared spectrophotometry and Gaia parallax to constrain stellar parameters, then applies it to characterize two confirmed hot Jupiters.","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"]