[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125659-en":3,"doc-seo-125659-105":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":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},125659,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Understanding Molecular Abundances in Star-Forming Regions Using Interpretable Machine Learning","Astrochemical modelling of the interstellar medium often relies on complex computational codes with adjustable parameters, making it difficult to determine how these inputs map to molecular abundances. This work applies interpretable machine learning via SHAP to quantify feature importance and reveal input–output relationships. A neural network emulator accelerates species abundance predictions, enabling efficient SHAP analysis across species and abundance ratios. Results show strong metallicity dependence for H2O and CO, distinct temperature regimes for NH3, and diagnostic ratios for HCN/HNC and HCN/CS linked to chemical pathways.","arXiv :2309 .06784v1 [ astro-ph .GA] 13 Sep 2023  \nUnderstanding Molecular Abundances in Star-Forming Regions Using Interpretable Machine Learning  \nJohannes Heyl 1★ , Joshua Butterworth2 , and Serena Viti2, 1  \n1 Department of Physics and Astronomy, University College London, Gower Street, WC1E 6BT, London, UK  \n2 Leiden Observatory, Leiden University, Huygens Laboratory, Niels Bohrweg 2, NL-2333 CA Leiden, The Netherlands  \nAccepted XXX. Received YYY; in original form ZZZ  \nABSTRACT  \nAstrochemical modelling of the interstellar medium typically makes use of complex computational codes with parameters whose values can be varied. It isnot always clear what the exact nature of the relationship is between these input parameters and the output molecular abundances. In this work, a feature importance analysis is conducted using SHapley Additive exPlanations (SHAP), an interpretable machine learning technique, to identify the most important physical parameters as well as their relationship with each output. The outputs are the abundances of species and ratios of abundances. In order to reduce the time taken for this process, a neural network emulator is trained to model each species’ output abundance and this emulator is used to perform the interpretable machine learning. SHAP is then used to further explore the relationship between the physical features and the abundances for the various species and ratios we considered. H2 O and CO’s gas phase abundances are found to strongly depend on the metallicity. NH3 has a strong temperature dependence, with there being two temperature regimes (\u003C 100 K and > 100K) . By analysing the chemical network, we relate this to the chemical reactions in our network and find the increased temperature results in increased efficiency of destruction pathways. We investigate the HCN/HNC ratio and show that it can be used as a cosmic thermometer, agreeing with the literature. This ratio is also found to be correlated with the metallicity. The HCN/CS ratio serves as a density tracer, but also has three separate temperature-dependence regimes, which are linked to the chemistry of the two molecules.  \nKey words: stars: abundances – astrochemistry – methods: statistical  \n1 INTRODUCTION  \nModelling the interstellar medium and star formation is often a complex matter. This is normally done using computational codes that take in a number of physical parameters and use these to integrate the system of coupled ordinary differential equations (ODEs) that represent a chemical network (Taquet et al. 2012; Ruaud et al. 2016; Holdship et al. 2017) . However, due to the non-linear nature of the chemistry, it is often unclear what the exact relationship is between the initial parameters and the output chemical abundances of the molecules of interest. This is often complicated by the fact that the various parameters have differing effects on the output abundances for different ranges.  \nIt has been customary in astrochemistry to consider grids of models in which the various parameters are varied (Taquet et al. 2012; Tunnard & Greve 2016; Viti 2017; Bianchi et al. 2019; James et al. 2020; Holdship & Viti 2022; Heyl et al. 2023). The time-consuming and computationally expensive nature of many computational codes often limits the total number of model evaluations possible. This makes drawing conclusions about the importance of various parameters difficult. In this work, we look to address both of these issues. We make use of SHapley Additive exPlanations (SHAP) (Lundberg  \n★ E-mail: [johannes.heyl.19@ucl.ac.uk](johannes.heyl.19@ucl.ac.uk)  \n& Lee 2017) to help improve our understanding of a chemical code. SHAP provides us with a means of understanding why a machine learning model outputs a particular value. By considering various combinations of inputs and outputs, these techniques will tell us what the relationship is. This has found use in astrophysics recently (Machado Poletti Valle et al. 2021; Ansari et al. 2022) in the","cbCailYdmXawj38f","https://ap.wps.com/l/cbCailYdmXawj38f","pdf",2110807,1,20,"English","en",105,"# Abstract\n# 1 Introduction\n## Chemical modelling challenges and parameter–abundance nonlinearity\n## Model grids and computational cost\n## SHAP for interpretability\n## Statistical emulation with neural network emulators","[{\"question\":\"How does the study determine which physical parameters matter most for molecular abundances?\",\"answer\":\"It uses SHAP (SHapley Additive exPlanations) to perform feature importance analysis and quantify how input parameters influence each molecular abundance and abundance ratio.\"},{\"question\":\"Why is a neural network emulator used in addition to the interpretability method?\",\"answer\":\"A neural network emulator is trained to rapidly predict species abundances, reducing the time required to evaluate many input combinations for the SHAP-based interpretability analysis.\"},{\"question\":\"What physical dependencies are reported for key molecular abundances and ratios?\",\"answer\":\"Gas-phase abundances of H2O and CO strongly depend on metallicity; NH3 shows two temperature regimes (\\u003c100 K and \\u003e100 K). HCN/HNC is discussed as a cosmic thermometer and is correlated with metallicity, while HCN/CS acts as a density tracer with multiple temperature-dependent regimes linked to chemistry.\"}]","Understanding Molecular Abundances in Star-Forming Regions Using Interpretable Machine Learning | PDF",1785900501,50,{"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},"understanding-molecular-abundances-in-star-forming-regions-using-interpretable-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/understanding-molecular-abundances-in-star-forming-regions-using-interpretable-machine-learning/125659/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the study determine which physical parameters matter most for molecular abundances?","Question",{"text":75,"@type":76},"It uses SHAP (SHapley Additive exPlanations) to perform feature importance analysis and quantify how input parameters influence each molecular abundance and abundance ratio.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is a neural network emulator used in addition to the interpretability method?",{"text":80,"@type":76},"A neural network emulator is trained to rapidly predict species abundances, reducing the time required to evaluate many input combinations for the SHAP-based interpretability analysis.",{"name":82,"@type":73,"acceptedAnswer":83},"What physical dependencies are reported for key molecular abundances and ratios?",{"text":84,"@type":76},"Gas-phase abundances of H2O and CO strongly depend on metallicity; NH3 shows two temperature regimes (\u003C100 K and >100 K). HCN/HNC is discussed as a cosmic thermometer and is correlated with metallicity, while HCN/CS acts as a density tracer with multiple temperature-dependent regimes linked to chemistry.","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},[93,97,101,105,110,114,119,122,126,129,133],{"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":29,"slug":113},6,"Technology","technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":21,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":21,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":106,"slug":136},19,"General","general"]