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dc.contributor.authorRistic, M.
dc.contributor.authorChampion, E.
dc.contributor.authorO'Shaughnessy, R.
dc.contributor.authorWollaeger, R.
dc.contributor.authorKorobkin, O.
dc.contributor.authorChase, E.A.
dc.contributor.authorFryer, C.L.
dc.contributor.authorHungerford, A.L.
dc.contributor.authorFontes, C.J.
dc.date.accessioned2022-03-17T01:56:56Z
dc.date.available2022-03-17T01:56:56Z
dc.date.issued2022
dc.identifier.citationRistic, M., Champion, E., O’Shaughnessy, R., Wollaeger, R., Korobkin, O., Chase, E. A., Fryer, C. L., Hungerford, A. L., & Fontes, C. J. (2022). Interpolating detailed simulations of kilonovae: Adaptive learning and parameter inference applications. Physical Review Research.
dc.identifier.issn2643-1564
dc.identifier.doi10.1103/PhysRevResearch.4.013046
dc.identifier.urihttp://hdl.handle.net/10150/663574
dc.description.abstractDetailed radiative transfer simulations of kilonovae are difficult to apply directly to observations; they only sparsely cover simulation parameters, such as the mass, velocity, morphology, and composition of the ejecta. On the other hand, semianalytic models for kilonovae can be evaluated continuously over model parameters, but neglect important physical details which are not incorporated in the simulations, thus introducing systematic bias. Starting with a grid of two-dimensional anisotropic simulations of kilonova light curves covering a wide range of ejecta properties, we apply adaptive learning techniques to iteratively choose new simulations and produce high-fidelity surrogate models for those simulations. These surrogate models allow for continuous evaluation across model parameters while retaining the microphysical details about the ejecta. Using a code formultimessenger inference developed by our group, we demonstrate how to use our interpolated models to infer kilonova parameters. Comparing to inferences using simplified analytic models, we recover different ejecta properties. We discuss the implications of this analysis which is qualitatively consistent with similar previous work using detailed ejecta opacity calculations and which illustrates systematic challenges for kilonova modeling. An associated data and code release provides our interpolated light-curve models, interpolation implementation which can be applied to reproduce our work or extend to new models, and our multimessenger parameter inference engine. © 2022 authors. Published by the American Physical Society.
dc.language.isoen
dc.publisherAmerican Physical Society
dc.rightsCopyright is held by the author(s) or the publisher. If your intended use exceeds the permitted uses specified by the license, contact the publisher for more information. Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleInterpolating detailed simulations of kilonovae: Adaptive learning and parameter inference applications
dc.typeArticle
dc.typetext
dc.contributor.departmentUniversity of Arizona
dc.identifier.journalPhysical Review Research
dc.description.noteOpen access journal
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
dc.eprint.versionFinal published version
dc.source.journaltitlePhysical Review Research
refterms.dateFOA2022-03-17T01:56:56Z


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Copyright is held by the author(s) or the publisher. If your intended use exceeds the permitted uses specified by the license, contact the publisher for more information. Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license.
Except where otherwise noted, this item's license is described as Copyright is held by the author(s) or the publisher. If your intended use exceeds the permitted uses specified by the license, contact the publisher for more information. Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license.