203 citations · 755 across the 13 of their papers we have counts for
14 papers
Local Bayesian Dirichlet mixing of imperfect models
Vojtech Kejzlar, Léo Neufcourt, Witold Nazarewicz
To improve the predictability of complex computational models in the experimentally-unknown domains, we propose a Bayesian statistical machine learning framework utilizing the Diri…
Neural Network Emulation of Spontaneous Fission
Daniel Lay, Eric Flynn, Samuel A. Giuliani +2
Large-scale computations of fission properties are an important ingredient for nuclear reaction network calculations simulating rapid neutron-capture process (the r process) nucleo…
Lightweight self-conjugate nucleus Zr
A. Hamaker, E. Leistenschneider, R. Jain +10
Protons and neutrons in the atomic nucleus move in shells analogous to the electronic shell structures of atoms. Nuclear shell structure varies across the nuclear landscape due to…
Statistical aspects of nuclear mass models
Vojtech Kejzlar, Léo Neufcourt, Witold Nazarewicz +1
We study the information content of nuclear masses from the perspective of global models of nuclear binding energies. To this end, we employ a number of statistical methods and dia…
Quantified limits of the nuclear landscape
Léo Neufcourt, Yuchen Cao, Samuel A. Giuliani +3
The chart of the nuclides is limited by particle drip lines beyond which nuclear stability to proton or neutron emission is lost. Predicting the range of particle-bound isotopes po…
Beyond the proton drip line: Bayesian analysis of proton-emitting nuclei
Léo Neufcourt, Yuchen Cao, Samuel Giuliani +3
The limits of the nuclear landscape are determined by nuclear binding energies. Beyond the proton drip lines, where the separation energy becomes negative, there is not enough bind…
Direct comparison between Bayesian and frequentist uncertainty quantification for nuclear reactions
G. B. King, A. E. Lovell, L. Neufcourt +1
Until recently, uncertainty quantification in low energy nuclear theory was typically performed using frequentist approaches. However in the last few years, the field has shifted t…
Bayesian averaging of computer models with domain discrepancies: a nuclear physics perspective
Vojtech Kejzlar, Léo Neufcourt, Taps Maiti +1
This article studies Bayesian model averaging (BMA) in the context of competing expensive computer models in a typical nuclear physics setup. While it is well known that BMA accoun…
Reconstruction of Storage Ring 's Linear Optics with Bayesian Inference
Yue Hao, Yongjun Li, Michael Balcewicz +2
A novel approach of accurately reconstructing storage ring's linear optics from turn-by-turn (TbT) data containing measurement error is introduced. This approach adopts a Bayesian…
Expansion of a filtration with a stochastic process: the information drift
Léo Neufcourt, Philip Protter
When expanding a filtration with a stochastic process it is easily possible for semimartingale no longer to remain semimartingales in the enlarged filtration. Y. Kchia and P. Prott…
Neutron drip line in the Ca region from Bayesian model averaging
Léo Neufcourt, Yuchen Cao, Witold Nazarewicz +2
The region of heavy calcium isotopes forms the frontier of experimental and theoretical nuclear structure research where the basic concepts of nuclear physics are put to stringent…
Bayesian approach to model-based extrapolation of nuclear observables
Léo Neufcourt, Yuchen Cao, Witold Nazarewicz +1
The mass, or binding energy, is the basis property of the atomic nucleus. It determines its stability, and reaction and decay rates. Quantifying the nuclear binding is important fo…
A third-moment theorem and precise asymptotics for variations of stationary Gaussian sequences
Leo Neufcourt, Frederi Viens
In two new papers (Bierme et al., 2013) and (Nourdin and Peccati, 2015), sharp general quantitative bounds \ are given to complement the well-known fourth moment theorem of Nualart…
Generalised arbitrage-free SVI volatility surfaces
Gaoyue Guo, Antoine Jacquier, Claude Martini +1
In this article we propose a generalisation of the recent work of Gatheral and Jacquier on explicit arbitrage-free parameterisations of implied volatility surfaces. We also discuss…