Bayes goes fast: Uncertainty quantification for a covariant energy density functional emulated by the reduced basis method

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dc.citation.articleNumber1054524en_US
dc.citation.journalTitleFrontiers in Physicsen_US
dc.citation.volumeNumber10en_US
dc.contributor.authorGiuliani, Pabloen_US
dc.contributor.authorGodbey, Kyleen_US
dc.contributor.authorBonilla, Edgarden_US
dc.contributor.authorViens, Frederien_US
dc.contributor.authorPiekarewicz, Jorgeen_US
dc.date.accessioned2023-02-23T18:46:11Zen_US
dc.date.available2023-02-23T18:46:11Zen_US
dc.date.issued2023en_US
dc.description.abstractA covariant energy density functional is calibrated using a principled Bayesian statistical framework informed by experimental binding energies and charge radii of several magic and semi-magic nuclei. The Bayesian sampling required for the calibration is enabled by the emulation of the high-fidelity model through the implementation of a reduced basis method (RBM)—a set of dimensionality reduction techniques that can speed up demanding calculations involving partial differential equations by several orders of magnitude. The RBM emulator we build—using only 100 evaluations of the high-fidelity model—is able to accurately reproduce the model calculations in tens of milliseconds on a personal computer, an increase in speed of nearly a factor of 3,300 when compared to the original solver. Besides the analysis of the posterior distribution of parameters, we present model calculations for masses and radii with properly estimated uncertainties. We also analyze the model correlation between the slope of the symmetry energy L and the neutron skin of 48Ca and 208Pb. The straightforward implementation and outstanding performance of the RBM makes it an ideal tool for assisting the nuclear theory community in providing reliable estimates with properly quantified uncertainties of physical observables. Such uncertainty quantification tools will become essential given the expected abundance of data from the recently inaugurated and future experimental and observational facilities.en_US
dc.identifier.citationGiuliani, Pablo, Godbey, Kyle, Bonilla, Edgard, et al.. "Bayes goes fast: Uncertainty quantification for a covariant energy density functional emulated by the reduced basis method." <i>Frontiers in Physics,</i> 10, (2023) Frontiers Media S.A.: https://doi.org/10.3389/fphy.2022.1054524.en_US
dc.identifier.digitalfphy-10-1054524en_US
dc.identifier.doihttps://doi.org/10.3389/fphy.2022.1054524en_US
dc.identifier.urihttps://hdl.handle.net/1911/114476en_US
dc.language.isoengen_US
dc.publisherFrontiers Media S.A.en_US
dc.rightsThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.en_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.titleBayes goes fast: Uncertainty quantification for a covariant energy density functional emulated by the reduced basis methoden_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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