NSR Query Results


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NSR database version of April 26, 2024.

Search: Author = F.Viens

Found 4 matches.

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2022CI08      J.Phys.(London) G49, 120502 (2022)

V.Cirigliano, Z.Davoudi, J.Engel, R.J.Furnstahl, G.Hagen, U.Heinz, H.Hergert, M.Horoi, C.W.Johnson, A.Lovato, E.Mereghetti, W.Nazarewicz, A.Nicholson, T.Papenbrock, S.Pastore, M.Plumlee, D.R.Phillips, P.E.Shanahan, S.R.Stroberg, F.Viens, A.Walker-Loud, K.A.Wendt, S.M.Wild

Towards precise and accurate calculations of neutrinoless double-beta decay

RADIOACTIVITY 48Ca(2β-); calculated neutrinoless nuclear matrix elements using chiral-EFT interactions, EDF, IBM, QRPA, SM-pf, SM-sdpf, SM-MBPT, RSM, QMC+SM, IM-GCM, VS-IMSRG, CCSD, CCSD-T1.

doi: 10.1088/1361-6471/aca03e
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2021PH05      J.Phys.(London) G48, 072001 (2021)

D.R.Phillips, R.J.Furnstahl, U.Heinz, T.Maiti, W.Nazarewicz, F.M.Nunes, M.Plumlee, M.T.Pratola, S.Pratt, F.G.Viens, S.M.Wild

Get on the BAND Wagon: a Bayesian framework for quantifying model uncertainties in nuclear dynamics

NUCLEAR REACTIONS 208Pb(p, p), E=30 MeV; calculated σ. Comparison with available data.

doi: 10.1088/1361-6471/abf1df
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2019NE02      Phys.Rev.Lett. 122, 062502 (2019)

L.Neufcourt, Y.Cao, W.Nazarewicz, E.Olsen, F.Viens

Neutron Drip Line in the Ca Region from Bayesian Model Averaging

NUCLEAR STRUCTURE 50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82Ca, 52Cl, 53Ar, 49S; calculated one- and two-neutron separation energies, posterior probability of existence of neutron-rich nuclei in the Ca region.

doi: 10.1103/PhysRevLett.122.062502
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2018NE08      Phys.Rev. C 98, 034318 (2018)

L.Neufcourt, Yu.Cao, W.Nazarewicz, F.Viens

Bayesian approach to model-based extrapolation of nuclear observables

ATOMIC MASSES Z=2-110, N=4-160; analyzed S(2n) of even-even nuclei from AME-2003 and AME-2016 evaluations, JYFLTRAP experimental data, and various global mass models; calculated S(2n), residuals of S(2n) for six global mass models, and S(2n) credibility interval to extrapolated nuclei with the chain of Sn nuclei as a representative example using Bayesian Gaussian processes and neural networks.

doi: 10.1103/PhysRevC.98.034318
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Note: The following list of authors and aliases matches the search parameter F.Viens: , F.G.VIENS