2017
DOI: 10.1103/physrevlett.118.032502
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Nucleus-Dependent Valence-Space Approach to Nuclear Structure

Abstract: We present a nucleus-dependent valence-space approach for calculating ground and excited states of nuclei, which generalizes the shell-model in-medium similarity renormalization group to an ensemble reference with fractionally filled orbitals. Because the ensemble is used only as a reference, and not to represent physical states, no symmetry restoration is required. This allows us to capture 3N forces among valence nucleons with a valence-space Hamiltonian specifically targeted to each nucleus of interest. Pre… Show more

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Cited by 251 publications
(344 citation statements)
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“…A new refinement of the targeted normal ordering procedure was presented in Ref. [198], improving once again the agreement between IMSRG+SM and large-scale MR-IMSRG calculations.…”
Section: Discussionmentioning
confidence: 99%
“…A new refinement of the targeted normal ordering procedure was presented in Ref. [198], improving once again the agreement between IMSRG+SM and large-scale MR-IMSRG calculations.…”
Section: Discussionmentioning
confidence: 99%
“…In this work, we extend this approach to the ensemble normal-ordering procedure outlined in Ref. [24], which accurately accounts for 3N forces between valence particles and reproduces results of large-space ab initio methods in all cases where those methods are reliable. In all reported results, we use the same initial NN and 3N Hamiltonians as in Ref.…”
Section: Modelsmentioning
confidence: 99%
“…As in Ref. [9], we choose USDA instead of USDB, since the latter is known to predict a too small excitation energy of the J π = 4 + state in 26 F. Valence-space IM-SRG has been shown to predict ground and excited states throughout the oxygen, fluorine, and neon isotopic chains [22][23][24]33]. Starting from nuclear forces derived from chiral effective field theory (EFT) [34,35], 3N forces between core and valence nucleons are typically captured by normal ordering with respect to the 16 O reference.…”
Section: Modelsmentioning
confidence: 99%
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