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Neural networks make accurate predictions but often fail to provide reliable uncertainty estimates, especially under covariate distribution shifts between training and testing. To address this problem, we propose a Bayesian framework for…

Machine Learning · Statistics 2025-12-22 Yuli Slavutsky , David M. Blei

Complex transition density can be constructed by a nuclear structure model with a complex basis and/or complex coefficient. In general, the complex transition density is converted to the real one with phase factor. In this study, we apply…

Nuclear Theory · Physics 2022-05-26 T. Furumoto

Bayesian methods have been very successful in quantifying uncertainty in physics-based problems in parameter estimation and prediction. In these cases, physical measurements y are modeled as the best fit of a physics-based model…

Data Analysis, Statistics and Probability · Physics 2015-02-06 Dave Higdon , Jordan D. McDonnell , Nicolas Schunck , Jason Sarich , Stefan M. Wild

A systematic study of parametric uncertainties in transfer reactions is performed using the recently developed uncertainty quantified global optical potential (KDUQ). We consider reactions on the doubly-magic spherical nucleus $^{48}$Ca and…

Nuclear Theory · Physics 2025-07-18 Chloë Hebborn , Filomena M. Nunes

Within a Bayesian statistical framework using a Gaussian Process emulator for an isospin-dependent Boltzmann-Uehling-Uhlenbeck (IBUU) transport model simulator of heavy-ion reactions at intermediate energies, we infer from the HADES proton…

Nuclear Theory · Physics 2023-08-15 Bao-An Li , Wen-Jie Xie

The dispersive optical-model is applied to transfer reactions. A systematic study of $(d,p)$ reactions on closed-shell nuclei using the finite-range adiabatic reaction model is performed at several beam energies and results are compared to…

Nuclear Theory · Physics 2011-10-25 N. B. Nguyen , S. J. Waldecker , F. M. Nunes , R. J. Charity , W. H. Dickhoff

Bayesian parameter estimation provides a systematic approach to compare heavy ion collision models with measurements, leading to constraints on the properties of nuclear matter with proper accounting of experimental and theoretical…

Nuclear Theory · Physics 2023-05-31 Brandon Weiss , Jean-François Paquet , Steffen A. Bass

Angular distributions of the elastic and inelastic deuteron-nucleus scattering off $^{24}$Mg, $^{28}$Si, $^{58}$Ni, $^{90}$Zr, $^{116}$Sn, and $^{208}$Pb have been measured at a beam energy of 98 MeV/nucleon, with the goal of constraining…

We introduce a Bayesian protocol based on artificial neural networks that is suitable for modeling inclusive electron-nucleus scattering on a variety of nuclear targets with quantified uncertainties. Unlike previous applications in the…

Nuclear Theory · Physics 2024-06-11 Joanna E. Sobczyk , Noemi Rocco , Alessandro Lovato

Model uncertainties, arising due to suppression of target excitations in the description of deuteron scattering and resulting in a modification of the two-body interactions in a three-body system, are investigated for several $(d,p)$…

Nuclear Theory · Physics 2021-09-22 M. J. Dinmore , N. K. Timofeyuk , J. S. Al-Khalili

Information about the physical properties of astrophysical objects cannot be measured directly but is inferred by interpreting spectroscopic observations in the context of atomic physics calculations. Ratios of emission lines, for example,…

We propose a general method to carry out a valid Bayesian analysis of a finite-dimensional `targeted' parameter in the presence of a finite-dimensional nuisance parameter. We apply our methods to causal inference based on estimating…

Methodology · Statistics 2026-02-03 Magid Sabbagh , David A. Stephens

Bayesian regression determines model parameters by minimizing the expected loss, an upper bound to the true generalization error. However, the loss ignores misspecification, where models are imperfect. Parameter uncertainties from Bayesian…

Machine Learning · Statistics 2024-11-07 Thomas D Swinburne , Danny Perez

The microscopic framework of time-dependent covariant density functional theory is applied to study multinucleon transfer reactions, with transfer probabilities calculated using the particle number projection method. It is found that…

Nuclear Theory · Physics 2024-02-23 D. D. Zhang , D. Vretenar , T. NikšIć , P. W. Zhao , J. Meng

We study posterior contraction behaviors for parameters of interest in the context of Bayesian mixture modeling, where the number of mixing components is unknown while the model itself may or may not be correctly specified. Two…

Statistics Theory · Mathematics 2019-01-17 Aritra Guha , Nhat Ho , XuanLong Nguyen

The Alpha Optical Model Potential (\aomp \!) is a phenomenological approach used to describe elastic scattering where multiple reaction channels are open. It is one of the most critical inputs for the calculation of thermonuclear reaction…

Accurate comparisons between theoretical models and experimental data are critical for scientific progress. However, inferred physical model parameters can vary significantly with the chosen physics model, highlighting the importance of…

High Energy Physics - Phenomenology · Physics 2025-10-27 Sunil Jaiswal , Chun Shen , Richard J. Furnstahl , Ulrich Heinz , Matthew T. Pratola

We apply to the nucleon-nucleus inelastic process a fully coherent microscopic multiple scattering approach. Our study addresses the complexities inherent in characterizing inelastic scattering events, offering a comprehensive theoretical…

Nuclear Theory · Physics 2026-03-30 Matteo Vorabbi , Michael Gennari , Paolo Finelli , Carlotta Giusti , Petr Navrátil

Theoretical uncertainties of various types are discussed for the nucleon-deuteron elastic scattering observables at the incoming nucleon laboratory energies up to 200 MeV. We are especially interested in the statistical errors arising from…

Nuclear Theory · Physics 2018-07-11 R. Skibinski , Yu. Volkotrub , J. Golak , K. Topolnicki , H. Witala

Microscopic optical model potential results for reaction cross sections of proton elastic scattering are presented. The applications cover the 10-1000 MeV energy range and consider both stable and unstable nuclei. The study is based on…

Nuclear Theory · Physics 2008-11-26 H. F. Arellano , M. Girod