[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124773-en":3,"doc-seo-124773-105":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},124773,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","A Statistical and Machine Learning Approach to the Study of Astrochemistry - Thesis","This doctoral thesis applies statistical and machine learning techniques to deepen understanding of astrochemical processes. Astrochemistry studies chemistry across the universe, and competing factors make molecular abundances hard to attribute to individual parameters. The work introduces multiple methods to gain insight: sensitivity analysis of hydrogenation effects on a glycine network, topology-based dimensionality reduction for Bayesian inference, physics-informed simplification via binding energies, MOPED-driven target selection, and machine-learning interpretability using SHAP. It further extends analyses to new JWST observations and compares inferred relationships to observational results.","UNIVERSITY COLLEGE LONDON  \nFaculty of Mathematical and Physical Sciences Department of Physics & Astronomy  \nA STATISTICAL AND MACHINE LEARNING APPROACH TO THE STUDY  \nOF ASTROCHEMISTRY  \nThesis submitted for the Degree of Doctor of Philosophy  \nby  \nJohannes Nasim Friedrich Heyl  \nSupervisors: Examiners:  \nProf. Serena Viti Prof. Wendy Brown  \nProf. Jonathan Tennyson Prof. Ingo Waldmann  \nSeptember 20, 2023  \nTo my family  \nI, Johannes Nasim Friedrich Heyl, confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis.  \nAbstract  \nThis thesis uses a variety of statistical and machine learning techniques to provide new insight into astrochemical processes. Astrochemistry is the study of chemistry in the universe. Due to the highly non-linear nature of a variety of competing factors, it is often difficult to understand the impact of any individual parameter on the abundance of molecules of interest. It is for this reason we present a number of techniques that provide insight.  \nChapter 2 is a chemical modelling study that considers the sensitivity of a glycine chemical network to the addition of two H2 addition reactions across a number of physical environments. This work considers the concept of a “hydrogen economy” within the context of chemical reaction networks and demonstrates that H2 decreases the abundance of glycine, one of the simplest amino acids, as well as its precursors.  \nChapter 3 considers a methodology that involves utilising the topology of a chemical network in order to accelerate the Bayesian inference problem by reducing the dimensionality of the parameters to be inferred at once. We demonstrate that a network can be simplified as well as split into smaller pieces for the inference problem by using a toy network.  \nChapter 4 considers how the dimensionality can be simplified by exploiting the physics of the underlying chemical reaction mechanisms. We do this by realising that the most pertinent reaction rate parameter is the binding energy of the more mobile species. This significantly reduces the dimensionality of the problem we have to solve.  \nChapter 5 builds on the work done in Chapters 3 and 4. The MOPED algorithm is utilised to identify which species should be prioritised for detection in order to reduce the variance of our binding energy posterior distributions.  \nChapter 6 introduces the use of machine learning interpretability to provide better insights into the relationships between the physical input parameters of a chemical code and the final abundances of various species. By identifying the relative importance of various parameters and quantifying this, we make qualitative comparisons to observations and demonstrate good agreement.  \nChapter 7 uses the same methods as in Chapters 4 , 5 and 6 in light of new JWST observations. The relationship between binding energies and the abundances of species is also explored using machine learning interpretability techniques.  \nImpact statement  \nThis thesis takes a multidisciplinary approach to the study of astrochemistry. Both Bayesian statistics and machine learning are used to provide novel insights into astrochemistry. The astrochemical community is the target audience for this thesis, which seeks to demonstrate how these powerful techniques can be used to generate a more sound understanding of observations and modelling.  \nIn Chapter 2 we perform a sensitivity analysis in which we investigate the effect of adding the H2 addition reactions C + H2 −−→ CH2 and CH + H2 −−→ CH3 to a glycine grain network. By demonstrating that we observe changes in the abundances of glycine, one of the simplest amino acids, and its precursors, we help explain why glycine has not been detected as well as how these addition reactions allow the network to tap into a previously unused hydrogen reservoir on the grains. This work has been published in Monthly Notices of th","cbCaijTnTo0iP0tD","https://ap.wps.com/l/cbCaijTnTo0iP0tD","pdf",9547557,1,244,"English","en",105,"# Abstract\n# Chapter 2: Sensitivity of a glycine chemical network to H2 addition reactions\n# Chapter 3: Topology-based acceleration of Bayesian inference\n# Chapter 4: Physics-driven dimensionality reduction using binding energies\n# Chapter 5: MOPED algorithm for prioritising detection targets\n# Chapter 6: Machine learning interpretability for parameter–abundance relationships\n# Chapter 7: Application to new JWST observations\n# Impact statement","[{\"question\":\"What is the main research focus of the thesis?\",\"answer\":\"The thesis focuses on using statistical and machine learning methods to produce new insights into astrochemical processes and how physical parameters influence molecular abundances.\"},{\"question\":\"How does the thesis approach Bayesian inference in chemical networks?\",\"answer\":\"It accelerates Bayesian inference by exploiting the topology of chemical networks to reduce the dimensionality of parameters inferred simultaneously.\"},{\"question\":\"Which machine learning interpretability method is used, and what does it provide?\",\"answer\":\"The thesis uses SHAP values to quantify and interpret the relative importance of physical input parameters for the final abundances of species, enabling qualitative comparisons with observations.\"}]","A Statistical and Machine Learning Approach to the Study of Astrochemistry - Thesis | PDF",1785894481,615,{"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},"a-statistical-and-machine-learning-approach-to-the-study-of-astrochemistry-thesis","",{"@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/a-statistical-and-machine-learning-approach-to-the-study-of-astrochemistry-thesis/124773/",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},"What is the main research focus of the thesis?","Question",{"text":75,"@type":76},"The thesis focuses on using statistical and machine learning methods to produce new insights into astrochemical processes and how physical parameters influence molecular abundances.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the thesis approach Bayesian inference in chemical networks?",{"text":80,"@type":76},"It accelerates Bayesian inference by exploiting the topology of chemical networks to reduce the dimensionality of parameters inferred simultaneously.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning interpretability method is used, and what does it provide?",{"text":84,"@type":76},"The thesis uses SHAP values to quantify and interpret the relative importance of physical input parameters for the final abundances of species, enabling qualitative comparisons with observations.","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"]