[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124668-en":3,"doc-seo-124668-105":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},124668,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Insights into Neutron Star Equation of State by Machine Learning","Machine learning is applied to constrain the equation of state of nuclear matter by mapping properties at saturation density and neutron-star observables. A neural-network platform is constructed to deliver reasonable predictions over the relevant parameter space and to provide new constraints on hadron interactions. The feasibility and efficiency are demonstrated using a Walecka-type relativistic mean-field approximation, where the network estimates model parameters with sufficient precision to reproduce nuclear-matter properties near saturation and global neutron-star characteristics.","arXiv :2309 . 11227v1 [nucl-th] 20 Sep 2023  \nInsights into neutron star equation of state by machine learning  \nLing-Jun Guo, 1, 2 Jia-Ying Xiong,2, 3 Yao Ma,2, 4, 5, ∗ and Yong-Liang Ma2, 5, 6,†  \n1 College of Physics, Jilin University, Changchun, 130012, China  \n2 School of Fundamental Physics and Mathematical Sciences,  \nHangzhou Institute for Advanced Study, UCAS, Hangzhou, 310024, China  \n3 Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China  \n4 University of Chinese Academy of Sciences, Beiing 100049, China  \n5 TaiJi Laboratory for Gravitational Wave Universe (Beijing/Hangzhou),  \nUniversity of Chinese Academy of Sciences, Beijing, 100049, China  \n6 International Center for Theoretical Physics Asia-Pacific (ICTP-AP) , UCAS, Beijing, 100190, China  \n(Dated: September 21, 2023)  \nDue to its powerful capability and high efficiency in big data analysis, machine learning has been applied in various fields. We construct a neural network platform to constrain the behaviors of the equation of state of nuclear matter with respect to the properties of nuclear matter at saturation density and the properties of neutron stars. It is found that the neural network is able to give reasonable predictions of parameter space and provide new hints into the constraints of hadron interactions. As a specific example, we take the relativistic mean field approximation in a widely accepted Walecka-type model to illustrate the feasibility and efficiency of the platform. The results show that the neural network can indeed estimate the parameters of the model at a certain precision such that both the properties of nuclear matter around saturation density and global properties of neutron stars can be saturated. The optimization of the present modularly designed neural network and extension to other effective models are straightforward.  \n∗  \n†  \n[mayao@ucas.ac.cn](mayao@ucas.ac.cn)[ ](mayao@ucas.ac.cn)[ylma@ucas.ac.cn](ylma@ucas.ac.cn)  \nI. INTRODUCTION AND MOTIVATION  \nWith the development of technologies, the amount of experimental data is increasing rapidly. These massive data are usually from different experimental targets and provide information on different aspects of the same system. Therefore, in order to get a complete understanding of the physics involved, one needs to combine these data and analyze them systematically. However, it’s usually a challenge for researchers to handle this kind of process due to the complexity of the data and the massive parameters in the model. The recently developed machine learning (ML) or artificial intelligence(AI)-driven technologies provide a way out. The ML methods have already earned credits in the fields of big data analysis due to their advantages of efficiency and adaptivity [1–4], and they have already been applied in many different fields of physics, e.g. , Refs. [5–13] and references therein.  \nIn nuclear physics, the properties of nuclear matter (NM) have been investigated for a long period but no consensus has been arrived at. Several fundamental questions are waiting for clarification, for example, what are the constituents of the NM, whether is a phase transition involved in the dense compact star matter or not, is dense nuclear matter in other states than Fermi-liquid, et al., (see, e.g., reviews Refs. [14–22] and references therein) . To resolve these questions, all the existing information from both experiments and theories should be combined in the corresponding analysis and a reliable technique, like ML developed here, is necessary.  \nThe constraints on nuclear matter come from both terrestrial experiments and astrophysical observations. Owing to the analysis of the structures of heavy nuclei, e.g. , 24 Mg, 90 Zr, 116 Sn and 208 Pb, and the data from heavy-ion collisions, one can provide information about NM properties around nuclear saturation density (n0 ≈ 0. 16fm−3) [23–26], such as the binding energy of nucleon e0 , the symmetry energy Esym, the incomp","cbCaimCsQtLNKYjd","https://ap.wps.com/l/cbCaimCsQtLNKYjd","pdf",5230089,1,12,"English","en",105,"# Introduction and Motivation\n## Machine learning for big-data physics\n## Nuclear matter constraints from experiments and astrophysics\n## Equation of state modeling and parameter fine-tuning\n## Purpose and application of the neural network platform","[{\"question\":\"How does machine learning help constrain the neutron star equation of state?\",\"answer\":\"It reduces the complexity of combining massive experimental and astrophysical information by learning mappings from nuclear-matter properties to neutron-star constraints, enabling efficient exploration of parameter space.\"},{\"question\":\"What inputs does the neural network use in this work?\",\"answer\":\"It constrains nuclear-matter behavior using properties at saturation density and neutron-star properties, linking them through a neural-network platform designed for the equation of state.\"},{\"question\":\"How is the method validated in the paper?\",\"answer\":\"The authors apply the approach to a Walecka-type relativistic mean-field model, showing the neural network can estimate model parameters with enough precision to match both near-saturation nuclear-matter properties and neutron-star global properties.\"}]","Insights into Neutron Star Equation of State by Machine Learning | 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does machine learning help constrain the neutron star equation of state?","Question",{"text":75,"@type":76},"It reduces the complexity of combining massive experimental and astrophysical information by learning mappings from nuclear-matter properties to neutron-star constraints, enabling efficient exploration of parameter space.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What inputs does the neural network use in this work?",{"text":80,"@type":76},"It constrains nuclear-matter behavior using properties at saturation density and neutron-star properties, linking them through a neural-network platform designed for the equation of state.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the method validated in the paper?",{"text":84,"@type":76},"The authors apply the approach to a Walecka-type relativistic mean-field model, showing the neural network can estimate model parameters with enough precision to match both near-saturation nuclear-matter properties and neutron-star 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