[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121938-en":3,"doc-seo-121938-105":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},121938,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","MACHINE LEARNING REFINEMENTS TO METALLICITY-DEPENDENT ISOTOPIC ABUNDANCES","The project uses machine learning to fit free parameters of a metallicity-dependent isotopic scaling model to elemental observations across multiple nucleosynthesis channels, including massive stars, Type Ia supernovae, the s-process, the r-process, and p-isotope production. Model quality is assessed by minimizing the reduced chi-squared between model predictions and data. The refined parameterization enables a metallicity-resolved table of 287 stable isotopic abundances, organized by astrophysical process, supporting reconstruction of chemical history under model constraints.","arXiv :2403 .02678v1 [ astro-ph .IM] 5 Mar 2024  \nMACHINE LEARNING REFINEMENTS TO METALLICITY-DEPENDENT ISOTOPIC ABUNDANCES  \nHaoxuan Sun  \nDepartment of Physics & Astronomy  \nMacalester College  \nSaint Paul, MN 55105  \n[hsun@macalester.edu](hsun@macalester.edu)  \nMarch 6, 2024  \nABSTRACT  \nThe project aims to use machine learning algorithms to fit the free parameters of an isotopic scaling model to elemental observations. The processes considered are massive star nucleosynthesis, Type Ia SNe, the s-process, the r-process, and p-isotope production. The analysis on the successful fits seeks to minimize the reduced chi squared between the model and the data. Based upon the successful refinement of the isotopic parameterized scaling model, a table providing the 287 stable isotopic abundances as a function of metallicity, separated into astrophysical processes, is useful for identifying the chemical history of them. The table provides a complete averaged chemical history for the Galaxy, subject to the underlying model constraints.  \n1 Introduction  \nThe yields of stellar simulations are dependent on the star’s initial isotopic composition. During hydrostatic burning phases, the initial composition is crucial for neutron capture reactions on initial metals, which affects the abundance of odd-z nuclei([10]) . The detailed stellar abundances influence the opacity of the star, which in turn influences the star’s structure as well as the loss of mass and angular momentum, thereby altering the late stellar evolution. Understanding γ-process abundances, which use s-and r-process isotopes as seeds, requires information on the initial abundances of heavy isotopes ([11] & [12]) . The objective of galactic chemical evolution (GCE) is to comprehend how the abundances of the elements and their isotopes changed from the big bang to the present day, and it can be used to obtain the isotopic abundances at any metallicity for use as inputs for stellar simulations. Traditional GCE models typically require nucleosynthesis yields from stellar simulations as inputs, but the problem with this approach is that in order to provide self-consistent nucleosynthesis yields, the stellar simulations require a full initial set of isotopic abundances ([13];[14];[15]) . The construction of an astrophysical model of all stable isotopes, based on physical principles for production sites and mechanisms, is a complementary strategy to conventional GCE methods. The completed model then provides the Galaxy’s average isotopic history, subject to the employed approximations ([19]) . Compared to full GCE calculations, this method is comparatively simple and approximative, but it improves upon the standard of scaling isotopic solar abundances by a constant factor.  \nThe study of chemical abundance in physics is an important aspect of understanding the composition of matter in the universe. Chemical abundance refers to the relative amounts of different chemical elements present in a given sample. This information is crucial for understanding the formation and evolution of stars, galaxies, and the universe as a whole. All stellar evolution models for nucleosynthesis necessitate a beginning point for isotopic abundance ([16]) . Except for the Sun, our understanding of the isotopic abundances of stars is generally incomplete. We offer parameters for models of a complete average isotopic decomposition as a function of metallicity, which are fitted to observational data, as opposed to the conventional forward galactic chemical evolution modeling that incorporates star yields beginning with big bang nucleosynthesis. This method of machine learning uses the grid search algorithm to identify the parameter values with the lowest reduced chi-square value, resulting in a better fit of the model to these real data. Our fittings of  \nA PREPRINT-MARCH 6, 2024  \nparameter finds the light elements with great accuracy, within a 8 .7% uncertainty compared with the original results but as wel","cbCaimcJQUXCLXVi","https://ap.wps.com/l/cbCaimcJQUXCLXVi","pdf",3660246,1,14,"English","en",105,"# Abstract\n# Introduction\n# Overview\n# Astrophysical Processes","[{\"question\":\"What is the main goal of the machine learning refinement in this study?\",\"answer\":\"Fit the free parameters of a metallicity-dependent isotopic scaling model to observational elemental data across several astrophysical production processes.\"},{\"question\":\"How is the fit quality evaluated in the project?\",\"answer\":\"By minimizing the reduced chi-squared between the model outputs and the observational data.\"},{\"question\":\"What output does the refined model provide for chemical history studies?\",\"answer\":\"A table of 287 stable isotopic abundances as a function of metallicity, separated by astrophysical processes, enabling an averaged Galactic chemical history within the model constraints.\"}]","MACHINE LEARNING REFINEMENTS TO METALLICITY-DEPENDENT ISOTOPIC ABUNDANCES | 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is the main goal of the machine learning refinement in this study?","Question",{"text":76,"@type":77},"Fit the free parameters of a metallicity-dependent isotopic scaling model to observational elemental data across several astrophysical production processes.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is the fit quality evaluated in the project?",{"text":81,"@type":77},"By minimizing the reduced chi-squared between the model outputs and the observational data.",{"name":83,"@type":74,"acceptedAnswer":84},"What output does the refined model provide for chemical history studies?",{"text":85,"@type":77},"A table of 287 stable isotopic abundances as a function of metallicity, separated by astrophysical processes, enabling an averaged Galactic chemical history within the model 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