[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128586-en":3,"doc-seo-128586-105":30,"detail-sidebar-cat-0-en-105":84},{"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},128586,549768064778,"Finn","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Uncertainty quantification in the machine-learning inference from neutron star probability distribution to the equation of state","Machine-learning inference and uncertainty quantification are developed for the neutron-star equation of state using the neutron-star probability distribution inferred directly from observations. Building on an earlier ensemble-learning prescription based on output variance from independently trained models, the work introduces a different uncertainty principle to validate prior conclusions. The method performs MC sampling of data to infer an EoS and convolves it with the observational probability distribution, enabling treatment of arbitrary distributions beyond Gaussian approximations. Recent multimessenger mass and radius data are used, including assessments of data-augmentation importance and prior dependence effects.","arXiv :2401 . 12688v2 [nucl-th] 1 Feb 2024  \nPrepared for submission to JHEP INT-PUB-24-001, YITP-24-05  \nUncertainty quantification in the machine-learning inference from neutron star probability distribution to the equation of state  \nYuki Fujimotoa Kenji Fukushimab Syo Kamatab Koichi Murasec,d  \na Institute for Nuclear Theory, University of Washington, Box 351550, Seattle, WA 98195, USA b Department of Physics, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan  \nc Yukawa Institute for Theoretical Physics, Kyoto University, Kyoto 606-8502 Japan d Department of Physics, Tokyo Metropolitan University, Hachioji 192-0397, Japan E-mail: [yfuji@uw.edu](yfuji@uw.edu), [fuku@nt.phys.s.u-tokyo.ac.jp](fuku@nt.phys.s.u-tokyo.ac.jp) ,  \n[skamata11phys@gmail.com](skamata11phys@gmail.com), [phys.murase@gmail.com](phys.murase@gmail.com)  \nAbstract: We discuss the machine-learning inference and uncertainty quantification for the equation of state (EoS) of the neutron star (NS) matter directly using the NS probability distribution from the observations. We previously proposed a prescription for uncertainty quantification based on ensemble learning by evaluating output variance from independently trained models. We adopt a different principle for uncertainty quantification to confirm the reliability of our previous results. To this end, we carry out the MC sampling of data to infer an EoS and take the convolution with the probability distribution of the observational data. In this newly proposed method, we can deal with arbitrary probability distribution not relying on the Gaussian approximation. We incorporate observational data from the recent multimessenger sources including precise mass measurements and radius measurements. We also quantify the importance of data augmentation and the effects of prior dependence.  \n\n| Contents\u003Cbr>1 Introduction\u003Cbr>2 Preview of the Present Work\u003Cbr>2.1 Previous method\u003Cbr>2.2 Subtleties in the previous method and a sketch of the new method\u003Cbr>2.3 Strategy of the present method\u003Cbr>3 Mapping M-R distributions to EoS using neural network\u003Cbr>3.1 Synopsis of our method\u003Cbr>3.2 EoS parametrization\u003Cbr>3.3 Dataset generation\u003Cbr>3.4 Neural-network models\u003Cbr>3.5 Observed properties of neutron stars\u003Cbr>4 Results for the Equation of State\u003Cbr>5 Discussions\u003Cbr>5.1 Test analysis with rescaled observational distributions\u003Cbr>5.2 Dependence on the number of segments in the EoS parametrization\u003Cbr>5.3 Training dataset sampling and prior dependence check\u003Cbr>5.4 Extension with the NS central pressure\u003Cbr>5.5 Noise dependence in data augmentation\u003Cbr>5.6 Alternative framework: direct evaluation of Jacobian\u003Cbr>6 Conclusions | 1\u003Cbr>5 5\u003Cbr>5 9\u003Cbr>11\u003Cbr>11\u003Cbr>12\u003Cbr>12\u003Cbr>13\u003Cbr>15\u003Cbr>15\u003Cbr>19\u003Cbr>19\u003Cbr>19\u003Cbr>20\u003Cbr>21\u003Cbr>24\u003Cbr>24\u003Cbr>26 |\n| --- | --- |\n\n1 Introduction  \nUnveiling properties of QCD (Quantum Chromodynamics) matter in the regime of finite baryon density is a vital challenge in modern nuclear physics. Despite various efforts over several decades, many aspects of finite-density QCD have remained elusive, such as the nature of QCD transitions (see refs. [1–3] for comprehensive reviews) . The equation of state (EoS) of dense matter is of central importance in describing the properties of QCD matter. Ample knowledge of the EoS, that is, the behavior of thermodynamic quantities in extreme environments, would help us reveal the properties of states emerging from QCD. This approach has indeed worked successfully in the case of QCD at small density and high temperature, where the EoS from numerical simulations of first-principles lattice QCD is the bedrock for the crossover from the hadronic state to a quark-gluon plasma [4–6] . By contrast, the finite-density state is inaccessible by the Monte-Carlo (MC) algorithm due  \nto the sign problem, though there are a few useful exceptions and one can perform the finite-density simulations in lattice QCD [7–12] .  \nNeutron stars (NSs) by far provide the most reliable and rob","cbCaii8m8P0jf3Ng","https://ap.wps.com/l/cbCaii8m8P0jf3Ng","pdf",2236974,1,36,"English","en",105,"# Introduction\n# Preview of the Present Work\n## Previous method\n## Subtleties in the previous method and a sketch of the new method\n## Strategy of the present method\n# Mapping M-R distributions to EoS using neural network\n## Synopsis of our method\n## EoS parametrization\n## Dataset generation\n## Neural-network models\n## Observed properties of neutron stars\n# Results for the Equation of State\n# Discussions\n## Test analysis with rescaled observational distributions\n## Dependence on the number of segments in the EoS parametrization\n## Training dataset sampling and prior dependence check\n## Extension with the NS central pressure\n## Noise dependence in data augmentation\n## Alternative framework: direct evaluation of Jacobian\n# Conclusions","[{\"question\":\"What observational inputs and factors are assessed in the results?\",\"answer\":\"The work incorporates recent multimessenger mass and radius measurements and quantifies how data augmentation affects outcomes and how sensitive the results are to prior dependence.\"}]","Uncertainty quantification in the machine-learning inference from neutron star probability distribution to the equation of state | 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