[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127725-en":3,"doc-seo-127725-105":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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":11,"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},127725,962084926284,"Aurora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","FlopPITy - Enabling self-consistent exoplanet atmospheric retrievals with machine learning","Interpreting exoplanet atmosphere observations to constrain physical and chemical properties typically relies on Bayesian retrieval, but the need for many model computations forces a trade-off between model complexity and runtime. The work implements and tests sequential neural posterior estimation (SNPE) to accelerate exoplanet atmospheric retrievals while enabling more computationally expensive forward models, such as those computing temperature structure via radiative transfer.","University of Groningen  \nFlopPITy  \nArdévol Martínez, F. ; Min, M. ; Huppenkothen, D. ; Kamp, I. ; Palmer, P. I.  \nPublished in:  \nAstronomy & Astrophysics  \nDOI:  \n10.1051/0004-6361/202348367  \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. , Min, M. , Huppenkothen, D. , Kamp, I. , & Palmer, P. I. (2024) . FlopPITy: Enabling selfconsistent exoplanet atmospheric retrievals with machine learning. Astronomy & Astrophysics , 681, Article L14 . [https://doi.org/10.1051/0004-6361/202348367](https://doi.org/10.1051/0004-6361/202348367)  \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: 01-01-2026  \nA&A, 681, L14 (2024)  \n[https:](https://doi.org/10.1051/0004-6361/202348367)[//](https://doi.org/10.1051/0004-6361/202348367)[doi.org](https://doi.org/10.1051/0004-6361/202348367)[/](https://doi.org/10.1051/0004-6361/202348367)[10.1051](https://doi.org/10.1051/0004-6361/202348367)[/](https://doi.org/10.1051/0004-6361/202348367)[0004-6361](https://doi.org/10.1051/0004-6361/202348367)[/](https://doi.org/10.1051/0004-6361/202348367)[202348367](https://doi.org/10.1051/0004-6361/202348367)  \n The Authors 2024  \n&~~t~~ronomy~~t~~rop~~h~~ys~~i~~cs  \nLETTER TO THE EDITOR  \nFlopPITy: Enabling self-consistent exoplanet atmospheric retrievals with machine learning  \nF. Ardévol Martínez 1,2 ,3 ,4, M. Min2 , D. Huppenkothen2 , I. Kamp 1, and P. I. Palmer3 ,4  \n1 Kapteyn Astronomical Institute, University of Groningen, Groningen, The Netherlands e-mail: [ardevol@astro.rug.nl](ardevol@astro.rug.nl)  \n2 Netherlands Space Research Institute (SRON), Leiden, The Netherlands  \n3 Centre for Exoplanet Science, University of Edinburgh, Edinburgh, UK  \n4 School of GeoSciences, University of Edinburgh, Edinburgh, UK  \nReceived 23 October 2023 / Accepted 20 December 2023  \nABSTRACT  \nContext. Interpreting the observations of exoplanet atmospheres to constrain physical and chemical properties is typically done using Bayesian retrieval techniques. Since these methods require many model computations, a compromise must be made between the model's complexity and its run time. Achieving this compromise leads to a simplification of many physical and chemical processes (e.g. parameterised temperature structure) .  \nAims. Here, we implement and test sequential neural posterior estimation (SNPE), a machine learning inference algorithm for atmospheric retrievals for exoplanets. The goal is to speed up retrievals so they can be run with more computationally expensive at","cbCaij4Zt4gEKgzC","https://ap.wps.com/l/cbCaij4Zt4gEKgzC","pdf",1410929,2,1,"English","en",105,"# Abstract\n## Context and goals\n## Methods\n## Results","[{\"question\":\"Why are atmospheric retrievals computationally expensive in Bayesian frameworks?\",\"answer\":\"Bayesian retrieval requires many forward model evaluations to compute the posterior, often reaching hundreds of thousands to millions until convergence.\"},{\"question\":\"What approach does the paper introduce to speed up retrievals?\",\"answer\":\"The paper implements sequential neural posterior estimation (SNPE), a machine learning inference method designed to produce reliable posteriors more efficiently.\"},{\"question\":\"What retrieval speed and validation results are reported?\",\"answer\":\"SNPE is reported to provide faithful posteriors, enabling self-consistent retrievals (e.g., for a synthetic brown dwarf) with only 50,000 forward evaluations and speedups roughly between 2× and 10× depending on model and data characteristics.\"}]","FlopPITy - Enabling self-consistent exoplanet atmospheric retrievals with machine learning | PDF",1785941244,20,{"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},"floppity-enabling-self-consistent-exoplanet-atmospheric-retrievals-with-machine-learning","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":20},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/floppity-enabling-self-consistent-exoplanet-atmospheric-retrievals-with-machine-learning/127725/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are atmospheric retrievals computationally expensive in Bayesian frameworks?","Question",{"text":75,"@type":76},"Bayesian retrieval requires many forward model evaluations to compute the posterior, often reaching hundreds of thousands to millions until convergence.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What approach does the paper introduce to speed up retrievals?",{"text":80,"@type":76},"The paper implements sequential neural posterior estimation (SNPE), a machine learning inference method designed to produce reliable posteriors more efficiently.",{"name":82,"@type":73,"acceptedAnswer":83},"What retrieval speed and validation results are reported?",{"text":84,"@type":76},"SNPE is reported to provide faithful posteriors, enabling self-consistent retrievals (e.g., for a synthetic brown dwarf) with only 50,000 forward evaluations and speedups roughly between 2× and 10× depending on model and data characteristics.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"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":29,"slug":126},9,"Religion & Spirituality","religion-spirituality",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":29,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]