no code implementations • 2 Nov 2023 • 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 Dirichlet distribution that combines results of several imperfect models.
no code implementations • 11 Feb 2020 • Vojtech Kejzlar, Léo Neufcourt, Witold Nazarewicz, Paul-Gerhard Reinhard
We study the information content of nuclear masses from the perspective of global models of nuclear binding energies.
no code implementations • 16 Jan 2020 • Léo Neufcourt, Yuchen Cao, Samuel A. Giuliani, Witold Nazarewicz, Erik Olsen, Oleg B. Tarasov
We use microscopic nuclear mass models and Bayesian methodology to provide quantified predictions of proton and neutron separation energies as well as Bayesian probabilities of existence throughout the nuclear landscape all the way to the particle drip lines.
no code implementations • 28 Oct 2019 • Léo Neufcourt, Yuchen Cao, Samuel Giuliani, Witold Nazarewicz, Erik Olsen, Oleg B. Tarasov
With the help of Bayesian methodology, we mix a family of nuclear mass models corrected with statistical emulators trained on the experimental mass measurements, in the proton-rich region of the nuclear chart.
no code implementations • 22 Jan 2019 • Léo Neufcourt, Yuchen Cao, Witold Nazarewicz, Erik Olsen, Frederi Viens
In particular, considering the current experimental information and current global mass models, we predict that $^{68}$Ca has an average posterior probability ${p_{ex}\approx76}$% to be bound to two-neutron emission while the nucleus $^{61}$Ca is likely to decay by emitting a neutron (${p_{ex}\approx 46}$ %).
no code implementations • 1 Jun 2018 • Léo Neufcourt, Yuchen Cao, Witold Nazarewicz, Frederi Viens
The increase in the predictive power is quite astonishing: the resulting rms deviations from experiment on the testing dataset are similar to those of more phenomenological models.