[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122713-en":3,"doc-seo-122713-105":30,"detail-sidebar-cat-0-en-105":92},{"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},122713,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Machine Learning Refinements to Metallicity-Dependent Isotopic Abundances - Article 14","The project develops machine learning techniques to refine an isotopic scaling model by fitting free parameters to elemental observations. Considered nucleosynthetic processes include massive star nucleosynthesis, Type Ia SNe, the s-process, the r-process, and p-isotope production. Model quality is evaluated by minimizing the reduced chi-squared between model outputs and data. The refined parameterized model enables construction of a metallicity-dependent table of 287 stable isotopic abundances, separated by astrophysical process, to support identification of the Galaxy’s chemical history under model constraints.","Article 14  \nJune 2023  \nMachine Learning Refinements to Metallicity-Dependent IsotopicAbundances  \nHaoxuan SunMacalester College,hsun@macalester.edu  \nFollow this and additional works at:https://digitalcommons.macalester.edu/mjpa  \nPart of the Astrophysics and Astronomy Commons,and the Physics Commons  \nRecommended Citation  \nSun,Haoxuan(2023)\"Machine Learning Refinements to Metallicity-Dependent Isotopic Abundances,\"Macalester Journal of Physics and Astronomy.Vol.11:Iss.1,Article 14.Available at:https://digitalcommons.macalester.edu/mjpa/vol11/iss1/14  \nThis Honors Project-Open Access is brought to you forfree and open access by the Physics and AstronomyDepartment at DigitalCommons@Macalester College.Ithas been accepted for inclusion in Macalester Journal ofPhysics and Astronomy by an authorized editor ofDigitalCommons@Macalester CollegeFor moreinformation,please contact scholarpub@macalester.edu.  \nMACALESTER COLLEGE  \nMachine Learning Refinements to Metallicity-Dependent Isotopic Abundances  \n# Abstract\n\nThe project aims to use machine learning algorithms to fit the free parameters of an isotopic scalingmodel to elemental observations.The processes considered are massive star nucleosynthesis,Type laSNe,the s-process,the r-process,and p-isotope production.The analysis on the successful fits seeks tominimize the reduced chi squared between the model and the data.Based upon the successfulrefinement of the isotopic parameterized scaling model,a table providing the 287 stable isotopicabundances as a function of metallicity,separated into astrophysical processes,is useful for identifyingthe chemical history of them.The table provides a complete averaged chemical history for the Galaxy,subject to the underlying model constraints.  \nMACALESTER COLLEGE  \n# Machine Learning Refinements toMetallicity-Dependent IsotopicAbundances\n\nbyHaoxuan Sun  \nin the  \nDepartment of Physics and AstronomyAdvisors:Christopher West  \nMay 2023  \n## Abstract\n\nDepartment of Physics and Astronomy  \nby Haoxuan Sun  \nThe project aims to use machine learning algorithms to fit the free parametersof an isotopic scaling model to elemental observations.The processes consideredare massive star nucleosynthesis,Type Ia SNe,the s-process,the r-process,andp-isotope production.The analysis on the successful fits seeks to minimize thereduced chi squared between the model and the data.Based upon the successfulrefinement of the isotopic parameterized scaling model,a table providing the 287stable isotopic abundances as a function of metallicity,separated into astrophysicalprocesses,is useful for identifying the chemical history of them.The table providesa complete averaged chemical history for the Galaxy,subject to the underlyingmodel constraints.  \nAcknowledgements  \nFirst,I would like to express my deepest gratitude to Professor Christopher Westfor providing me with the opportunity to work on this project and for his invalu-able guidance and extensive knowledge.I am also grateful to my honors projectcommittee,comprised of Professor Will Mitchell,for offering valuable feedbackand motivating me to refine my work further.In addition,I extend my thanksto Professors John Cannon and Anna Williams for mentoring me throughout myjourney in the Macalester Physics &Astronomy Department.I would also like toacknowledge Yixiao Wang for his exceptional computer science skills in developingthe isotopic table and my dear Ziyi Wang for their unwavering support during thisproject.I am grateful to my parents for nurturing and supporting me throughoutmy life.Lastly,I want to thank everyone at the Macalester Physics &AstronomyDepartment for their assistance and contribution to my academic success.  \nContents  \ni  \nAbstract  \nii  \nAcknowledgements  \nList of Figures  \nV  \nvi  \nList of Tables  \n1  \n1 Introduction  \n1.1 Overview………………………………………………………………………………………………………………………………………………2  \n1.2 Astrophysical Processes………………………………………………………………………………………3  \n1.3 Model Description ……………………………………………………………………","cbCaicoPPhtx3gX7","https://ap.wps.com/l/cbCaicoPPhtx3gX7","pdf",41284002,1,134,"English","en",105,"# Abstract\n# Acknowledgements\n# List of Figures\n# List of Tables\n# 1 Introduction\n## 1.1 Overview\n## 1.2 Astrophysical Processes\n## 1.3 Model Description\n# 2 Methodology and Processing\n## 2.1 Methodology of Work\n## 2.2 Machine Learning\n## 2.3 Grid Search\n## 2.4 Random Forest\n# 3 Analysis\n## 3.1 Analysis on Grid Search Algorithm\n## 3.2 Analysis on Random Forest Algorithm\n## 3.3 Visualizing the Isotopic Table\n# 4 Results and Discussion\n## 4.1 Comparison\n## 4.2 Limitations\n## 4.2.1 Data Sources\n## 4.2.2 Machine Learning Algorithm\n## 4.3 Uncertainty Analysis\n# 5 Conclusion\n# Bibliography","[{\"question\":\"What does the project refine using machine learning?\",\"answer\":\"It uses machine learning to fit the free parameters of an isotopic scaling model to elemental observations.\"},{\"question\":\"Which astrophysical processes are included in the model?\",\"answer\":\"The analysis considers massive star nucleosynthesis, Type Ia SNe, the s-process, the r-process, and p-isotope production.\"},{\"question\":\"How is success of the model fitting evaluated?\",\"answer\":\"Successful fits are assessed by minimizing the reduced chi-squared between the model and the data.\"}]","Machine Learning Refinements to Metallicity-Dependent Isotopic Abundances - Article 14 | 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