[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123042-en":3,"doc-seo-123042-105":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":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},123042,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine learning for exoplanet characterisation in the JWST era","University of Groningen dissertation propositions focus on how machine learning supports exoplanet atmospheric characterisation in the JWST era. Key points include addressing overconfident posterior estimates from Multinest, improving likelihood-free inference for computationally prohibitive studies, and highlighting the need to incorporate unknown physical processes or opacity sources for cold Y dwarf atmospheres. The work also emphasizes modelling interdependence across atmospheric processes and using machine learning to align high-quality observations with complex atmospheric models for experimental model testing.","University of Groningen  \nMachine learning for exoplanet characterisation in the JWST era  \nArdévol Martínez, Francisco  \nDOI:  \n10.33612/diss.1097917125  \nIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nPublication date: 2024  \nLink to publication in University of Groningen/UMCG research database  \nCitation for published version (APA):  \nArdévol Martínez, F. (2024) . Machine learning for exoplanet characterisation in the JWST era. [Thesis fully internal (DIV), University of Groningen] . University of Groningen. [https://doi.org/10.33612/diss.1097917125](https://doi.org/10.33612/diss.1097917125)  \nCopyright  \nOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons) .  \nThe publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: [https://www.rug.nl/library/open-access/self-archiving-pure/taverne](https://www.rug.nl/library/open-access/self-archiving-pure/taverne)amendment.  \nTake-down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownloaded from the University of Groningen/U MCG research database (Pure): [http://www.rug. nl/research/portal. For technical reasons the](http://www.rug. nl/research/portal. For technical reasons the)[ ](http://www.rug. nl/research/portal. For technical reasons the)[number of authors shown on this cover page is limited to 10 maximum.](number of authors shown on this cover page is limited to 10 maximum.)  \nDownload date: 29-12-2025  \nPropositions  \naccompanying the dissertation  \nMACHINE LEARNING FOR EXOPLANET CHARACTERISATION IN THE JWST  \nERA  \n1. Posterior distributions obtained with Multinest can be overconfident. This is significantly worse for out-of-distribution observations (Chapter 2) .  \n2. Machine learning-based likelihood-free inference enables previously computationally unfeasible studies (Chapter 3) .  \n3. The modelling of cold Y dwarf atmospheres necessitates the inclusion of yet unknown physical processes or opacity sources (Chapter 4) .  \n4. It is crucial to consider the interdependence of different atmospheric processes to accurately characterise exoplanet atmospheres (Chapter 5) .  \n5. Machine learning is a necessary tool to match current high quality exoplanet atmospheric observations with complex atmospheric models. This, in turn, is essential to ‘experimentally’ test our models (This thesis) .  \n6. No discriminatory or abusive behaviour should be justified or excused on the basis of the scientific results it is conducive to.  \n7. If failure is not an option, you are not climbing hard enough grades.  \n8. Vacations do not need to be earned or deserved. They should always betaken when needed or wanted.  \n9. Science is done by, and affects, people. People live in a society within a given political context. Science can therefore not be apolitical.  \nFrancisco Ardévol Martínez","cbCaidWEOHuNC0CE","https://ap.wps.com/l/cbCaidWEOHuNC0CE","pdf",260218,1,2,"English","en",105,"# Propositions\n## Multinest posterior overconfidence and out-of-distribution observations\n## Likelihood-free inference for computationally unfeasible studies\n## Cold Y dwarf atmospheres and missing physical processes or opacity sources\n## Interdependence of atmospheric processes for accurate characterisation\n## Matching JWST observations to complex atmospheric models\n## Scientific responsibility and ethics","[{\"question\":\"What issue is noted about Multinest posterior distributions in the dissertation propositions?\",\"answer\":\"Posterior distributions obtained with Multinest can be overconfident, and this is significantly worse for out-of-distribution observations (Chapter 2).\"},{\"question\":\"How does machine learning-based likelihood-free inference help in this work?\",\"answer\":\"It enables previously computationally unfeasible studies (Chapter 3).\"},{\"question\":\"Why are unknown physical processes or opacity sources important for modelling cold Y dwarf atmospheres?\",\"answer\":\"The modelling necessitates their inclusion because they affect how cold Y dwarf atmospheres behave (Chapter 4).\"}]","Machine learning for exoplanet characterisation in the JWST era | 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issue is noted about Multinest posterior distributions in the dissertation propositions?","Question",{"text":74,"@type":75},"Posterior distributions obtained with Multinest can be overconfident, and this is significantly worse for out-of-distribution observations (Chapter 2).","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does machine learning-based likelihood-free inference help in this work?",{"text":79,"@type":75},"It enables previously computationally unfeasible studies (Chapter 3).",{"name":81,"@type":72,"acceptedAnswer":82},"Why are unknown physical processes or opacity sources important for modelling cold Y dwarf atmospheres?",{"text":83,"@type":75},"The modelling necessitates their inclusion because they affect how cold Y dwarf atmospheres behave (Chapter 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