Properties of the neutron star crust informed by nuclear structure data

[2505.16929] Properties of the neutron star crust informed by nuclear structure data Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Nuclear Theory arXiv:2505.16929 (nucl-th) [Submitted on 22 May 2025 ( v1 ), last revised 14 Apr 2026 (this version, v2)] Title: Properties of the neutron star crust informed by nuclear structure data Authors: Pietro Klausner , Marco Antonelli , Francesca Gulminelli View a PDF of the paper titled Properties of the neutron star crust informed by nuclear structure data, by Pietro Klausner and 2 other authors View PDF HTML (experimental) Abstract: We perform a Bayesian analysis of the neutron star (NS) equation of state (EoS) based on a wide set of Skyrme functionals, derived from previous nuclear physics inferences. The novelty of this approach lies in starting from the full multidimensional posterior distribution of nuclear matter parameters, consistent with a comprehensive set of static and dynamic nuclear structure observables. We construct unified EoSs for $npe\mu$ matter, where the inner crust of the NS is treated using an extended Thomas-Fermi method, providing for the first time a fully consistent Bayesian treatment of the correlation of bulk with surface as well as with spin-orbit and effective mass parameters. We then employ a standard Bayesian framework to identify those EoSs that satisfy astrophysical constraints from NS mass measurements, the tidal deformability from GW170817, and NICER mass-radius observations. We also examine NS observables, such as the crustal moment of inertia, which is crucial in understanding pulsar glitches. Compared to previous works, we observe an increase in both the NS surface thickness and the crustal moment of inertia. Comments: 19 pages, 14 figures Subjects: Nuclear Theory (nucl-th) ; High Energy Astrophysical Phenomena (astro-ph.HE) Cite as: arXiv:2505.16929 [nucl-th] (or arXiv:2505.16929v2 [nucl-th] for this version) https://doi.org/10.48550/arXiv.2505.16929 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Phys. Rev. C 113, 025808. Published 23 February, 2026 Related DOI : https://doi.org/10.1103/mm6h-3jqs Focus to learn more DOI(s) linking to related resources Submission history From: Pietro Klausner [ view email ] [v1] Thu, 22 May 2025 17:22:16 UTC (14,126 KB) [v2] Tue, 14 Apr 2026 09:24:59 UTC (3,694 KB) Full-text links: Access Paper: View a PDF of the paper titled Properties of the neutron star crust informed by nuclear structure data, by Pietro Klausner and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: nucl-th < prev | next > new | recent | 2025-05 Change to browse by: astro-ph astro-ph.HE References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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