[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119132-en":3,"doc-seo-119132-105":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},119132,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Predicting the Rotational Dependence of Line Broadening Using Machine Learning","Correct pressure broadening is essential for modelling radiative transfer in atmospheres, but data are lacking for many exotic molecules expected in exoplanetary atmospheres. This work applies modern machine learning to mass-produce pressure broadening parameters for large sets of molecules in the ExoMol database, training on empirical air-broadening data from HITRAN. A computationally inexpensive production method is developed and validated on unseen active molecules with 69% accuracy, enabling a more complete and accurate treatment of line broadening in ExoMol and offering suggestions to improve air-broadening parameters for atmospheric species.","| Predicting the rotational dependence of line broadening using machine learning\u003Cbr>Elizabeth R. Guest, Jonathan Tennyson ∗, Sergei N. Yurchenko\u003Cbr>Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, UK |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Dataset link: 10.5281/zenodo.10631728 |  | Correct pressure broadening is essential for modelling radiative transfer in atmospheres, however data are lacking for the many exotic molecules expected in exoplanetary atmospheres. Here we explore modern machine learning methods to mass produce pressure broadening parameters for a large number of molecules in the ExoMol data base. To this end, state-of-the-art machine learning models are used to fit to existing, empirical air-broadening data from the HITRAN database. A computationally cheap method for large-scale production of pressure broadening parameters is developed, which is shown to be reasonably (69%) accurate for unseen active molecules. This method has been used to augment the previously insufficient ExoMol line broadening diet, providing air-broadening data for all ExoMol molecules, so that the ExoMol database has a full and more accurate treatment of line broadening. Suggestions are made for improved air-broadening parameters for species present in atmospheric databases. |\n| Keywords:\u003Cbr>Machine learning\u003Cbr>Line broadening |  |  |\n\n1. Introduction  \nThe characterisation and modelling of exoplanetary atmospheres require large volumes of laboratory spectroscopic data [1,2]. Simulations have demonstrated the need to deal correctly with line broadening in the atmospheres of exoplanets [3,4]. In general, exoplanetary atmospheric models are limited by insufficient data; particular areas where more information is needed include collisional broadening and line mixing parameters. Indeed, the lack of suitable collision broadening parameters is given as the number one requirement in a recent review of laboratory data needs to aid understanding exoplanetary atmospheres by Fortney et al. [5]. Current studies of hot atmospheresuse at best qualitative estimates of pressure-broadening parameters for many molecules and molecular ions. Exoplanets have many potential compositions [6] and have been observed at wide ranges of temperatures [7] with hot planets orbiting close to their host stars providing the most reliable observations. Uncommon molecules on Earth are expected to be highly important for exoplanetary atmospheric processes, such as metal hydrides and oxides. In order to observe the huge expected variety of molecular species, a huge amount of spectroscopic data must be produced [8,9] to match the spectral features observed. This paper represents a first step towards meeting the pressure broadening portion of this need.  \nThe necessity for this work is due of the sparsity of data for pressure broadening parameters as pressure broadening is unknown for the majority of collisional pairs. Many papers have laid out the need for  \n∗ Corresponding author.  \nE-mail address: [j.tennyson@ucl.ac.uk](j.tennyson@ucl.ac.uk) (J. Tennyson).  \nmore data for all molecule broadener pairs [3,5,10,11]. The importance of pressure broadening increases with more modern telescopes, asthe impact of broadening scales with resolution. The differences in spectral cross sections due to pressure broadening have been shown tobe significant [12–14]. For instance the James Webb Space Telescope (JWST), with resolution 􀁒 ∼ 1000 − 3000, will have errors in cross sections of up to 40% when pressure broadening data is missing [7]. Pressure broadening is also important because exoplanet spectra are optically thick. When spectral lines are fully saturated, line intensity alone is no longer sufficient to determine opacity. Pressure broadening is therefore an important component of the opacity in optically thick conditions. The importance of pressure broadening as a spectral parameter is therefore clear. A","cbCaihIJCcpTmOOd","https://ap.wps.com/l/cbCaihIJCcpTmOOd","pdf",1726649,1,14,"English","en",105,"# Introduction\n## Background: exoplanet atmospheres and line broadening needs\n## Data sources: HITRAN and ExoMol\n## Line-shape model and focus on pressure broadening","[{\"question\":\"Why is pressure broadening critical for exoplanet atmosphere modelling?\",\"answer\":\"Pressure broadening affects radiative transfer and spectral opacity, especially under optically thick conditions where line intensity alone cannot determine opacity accurately. Missing pressure broadening data can lead to large errors in spectral cross sections.\"},{\"question\":\"How does the study generate pressure broadening parameters at scale?\",\"answer\":\"It uses modern machine learning models to fit existing empirical air-broadening measurements from the HITRAN database, producing pressure broadening parameters for many molecules in the ExoMol database.\"},{\"question\":\"How accurate is the proposed computationally cheap production method?\",\"answer\":\"The method reaches reasonably good accuracy, reported as 69% for unseen active molecules.\"}]","Predicting the Rotational Dependence of Line Broadening Using Machine Learning | PDF",1785722576,35,{"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},"predicting-the-rotational-dependence-of-line-broadening-using-machine-learning","",{"@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/predicting-the-rotational-dependence-of-line-broadening-using-machine-learning/119132/",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},"Why is pressure broadening critical for exoplanet atmosphere modelling?","Question",{"text":75,"@type":76},"Pressure broadening affects radiative transfer and spectral opacity, especially under optically thick conditions where line intensity alone cannot determine opacity accurately. Missing pressure broadening data can lead to large errors in spectral cross sections.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study generate pressure broadening parameters at scale?",{"text":80,"@type":76},"It uses modern machine learning models to fit existing empirical air-broadening measurements from the HITRAN database, producing pressure broadening parameters for many molecules in the ExoMol database.",{"name":82,"@type":73,"acceptedAnswer":83},"How accurate is the proposed computationally cheap production method?",{"text":84,"@type":76},"The method reaches reasonably good accuracy, reported as 69% for unseen active molecules.","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"]