[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123781-en":3,"doc-seo-123781-105":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},123781,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Deducing neutron star equation of state from telescope spectra with machine-learning-derived likelihoods","Neutron-star interiors reach densities and temperatures beyond terrestrial experiments, making them essential laboratories for nuclear physics and dense-matter studies. Their internal pressure and density control macroscopic structure, which in turn shapes the spectra observed by telescopes, creating a complex and partially intractable inference problem. The work replaces intractable likelihood components with machine-learning models trained on simulated stars, enabling full likelihood evaluation, maximum a posteriori estimation, and parameter scans. The method infers stellar mass and radius from a spectrum and equation-of-state parameters from multiple spectra, achieving 11.8% narrower residual widths than regression in realistic scenarios while releasing neural networks for fast simulation of neutron-star properties and spectra.","UC Irvine  \nUC Irvine Previously Published Works  \nTitle  \nDeducing neutron star equation of state from telescope spectra with machine-learningderived likelihoods  \nPermalink  \n[https://escholarship.org/uc/item/7567v6n4](https://escholarship.org/uc/item/7567v6n4)  \nJournal  \nJournal of Cosmology and Astroparticle Physics, 2023(12)  \nISSN  \n1475-7516  \nAuthors  \nFarrell, Delaney  \nBaldi, Pierre Ott, Jordan  \net al.  \nPublication Date  \n2023-12-01  \nDOI  \n10.1088/1475-7516/2023/12/022  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons Attribution License, available at [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nJournal of Cosmology and Astroparticle Physics  \nPAPER • OPEN ACCESS  \nDeducing neutron star equation of state from telescope spectra with machine-learning-derived likelihoods  \nTo cite this article: Delaney Farrell et al JCAP12(2023)022  \nView the article online for updates and enhancements.  \nThis content was downloaded from IP address [174.66.149.205](174.66.149.205) on 12/12/2023 at 18:23  \nJA  Calosmology and Astroparticle Physics  \nDeducing neutron star equation of state from telescope spectra with machine-learning-derived likelihoods  \nDelaney Farrell,a Pierre Baldi,b Jordan Ott,b Aishik Ghosh,c,d Andrew W. Steiner,e,f Atharva Kavitkar,g Lee Lindblom,h Daniel Whitesonc and Fridolin Webera,h  \na Department of Physics, San Diego State University, San Diego, CA 92115, U.S.A.  \nb Department of Computer Science, University of California Irvine, Irvine, CA 92697, U.S.A.  \nc Department of Physics and Astronomy, University of California Irvine, Irvine, CA 92697, U.S.A.  \nd Physics Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, U.S.A.  \ne Department of Physics and Astronomy, University of Tennessee, Knoxville, TN 37996, U.S.A.  \nf Physics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, U.S.A.  \ng Department of Computer Science, TU Kaiserslautern, Kaiserslautern, Germany  \nh Center for Astrophysics and Space Sciences, University of California at San Diego, San Diego, CA 92093, U.S.A.  \nE-mail: [dfarrell@sdsu.edu](dfarrell@sdsu.edu), [pfbaldi@ics.uci.edu](pfbaldi@ics.uci.edu), [jott1@uci.edu](jott1@uci.edu), [aishikg@uci.edu](aishikg@uci.edu),  \n[asteiner@utk.edu](asteiner@utk.edu), [atharva.m.kavitkar@gmail.com](atharva.m.kavitkar@gmail.com), [llindblom@ucsd.edu](llindblom@ucsd.edu),  \n[daniel@uci.edu](daniel@uci.edu), [fweber@sdsu.edu](fweber@sdsu.edu)  \nReceived May 26, 2023 Revised August 25, 2023 Accepted November 14, 2023 Published December 12, 2023  \nAbstract. The interiors of neutron stars reach densities and temperatures beyond the limits of terrestrial experiments, providing vital laboratories for probing nuclear physics. While the star’s interior is not directly observable, its pressure and density determine the star’s macroscopic structure which affects the spectra observed in telescopes. The relationship between the observations and the internal state is complex and partially intractable, presenting difficulties for inference. Previous work has focused on the regression from stellar spectra of  \n⃝c 2023 The Author(s) . Published by IOP Publishing  \nLtd on behalf of Sissa Medialab. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.  \n[https://doi.org/10.1088/1475-7516/2023/12/022](https://doi.org/10.1088/1475-7516/2023/12/022)  \nJ CAP12(2023)022  \nparameters describing the internal state. We demonstrate a calculation of the full likelihood of the internal state parameters given observations, accomplished by replacing intractable elements with machine learning models trained on s","cbCaifiOXPlNvDbU","https://ap.wps.com/l/cbCaifiOXPlNvDbU","pdf",2758438,1,26,"English","en",105,"# Contents\n## Introduction\n## Background\n## Machine learning\n## Training data\n### Generation of equation of state\n### Modeling spectra\n### Nuisance parameters\n## Machine-learning derived likelihood calculation\n## Stellar mass and radius inference\n### Learning the model f for stellar spectra\n### Results\n## Equation of state inference\n### Learning the model h λ[M ] for stellar radius\n### Results\n## Discussion\n## Conclusion\n## Hyperparameter optimization","[{\"question\":\"What problem does the paper address in neutron-star parameter inference?\",\"answer\":\"It addresses how to infer internal-state parameters from telescope spectra when the mapping from observations to interior physics is complex and partially intractable.\"},{\"question\":\"How does the method turn simulations into a usable likelihood for inference?\",\"answer\":\"It trains machine-learning models on simulated stars to replace intractable elements, enabling evaluation of the full likelihood for internal parameters given observations.\"},{\"question\":\"What quantities does the technique estimate and how is performance assessed?\",\"answer\":\"It infers stellar mass and radius from an individual spectrum and equation-of-state parameters from sets of spectra, achieving more precise results than pure regression by reducing residual widths by 11.8% in a realistic scenario.\"}]","Deducing neutron star equation of state from telescope spectra with machine-learning-derived likelihoods | 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problem does the paper address in neutron-star parameter inference?","Question",{"text":75,"@type":76},"It addresses how to infer internal-state parameters from telescope spectra when the mapping from observations to interior physics is complex and partially intractable.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the method turn simulations into a usable likelihood for inference?",{"text":80,"@type":76},"It trains machine-learning models on simulated stars to replace intractable elements, enabling evaluation of the full likelihood for internal parameters given observations.",{"name":82,"@type":73,"acceptedAnswer":83},"What quantities does the technique estimate and how is performance assessed?",{"text":84,"@type":76},"It infers stellar mass and radius from an individual spectrum and equation-of-state parameters from sets of spectra, achieving more precise results than pure regression by reducing residual widths by 11.8% in a realistic 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