Related papers: Constraining Transfer Cross Sections Using Bayes' …
A microscopic analysis of the optical potentials (OPs) and cross sections of quasielastic scattering of $^{12,14}$Be on $^{12}$C at 56 MeV/nucleon and on protons at energy near 700 MeV is carried out. For lower energy scattering the real…
We study nuclear reaction cross-sections for stable and unstable projectiles and targets within Glauber model, using densities obtained from various relativistic mean field formalisms. The calculated cross-sections are compared with the…
We study the inference problem in the group testing to identify defective items from the perspective of the decision theory. We introduce Bayesian inference and consider the Bayesian optimal setting in which the true generative process of…
Bayesian optimal design is considered for experiments where the response distribution depends on the solution to a system of non-linear ordinary differential equations. The motivation is an experiment to estimate parameters in the equations…
Elastic $^{16}$O+$^{12}$C scattering is known to exhibit the nuclear rainbow pattern at incident energies $E_\text{lab}\gtrsim 200$ MeV, with the Airy structure of the far-side scattering cross section clearly seen at medium and large…
This paper studies a problem of Bayesian parameter estimation for a sequence of scaled counting processes whose weak limit is a Brownian motion with an unknown drift. The main result of the paper is that the limit of the posterior…
The multinucleon transfer reaction in the collisions of $^{40}$Ca+$^{124}$Sn at $E_{\textrm{c.m.}}=128.5$ MeV is investigated by using the improved quantum molecular dynamics model. The measured angular distributions and isotopic…
Microscopic optical potentials have been successful in describing nucleon-nucleus and nucleus-nucleus scattering. Some essential ingredients of the framework, however, have not been examined in detail. Applicability of the microscopic…
Developing reliable interatomic potential models with quantified predictive accuracy is crucial for atomistic simulations. Commonly used potentials, such as those constructed through the embedded atom method (EAM), are derived from…
The recent availability of precisely measured fusion cross-sections has enabled the extraction of a representation of the distribution of barriers encountered during fusion. These representations, obtained from a variety of reactions,…
One major impediment to the wider use of deep learning for clinical decision making is the difficulty of assigning a level of confidence to model predictions. Currently, deep Bayesian neural networks and sparse Gaussian processes are the…
A key problem in the theory of meta-learning is to understand how the task distributions influence transfer risk, the expected error of a meta-learner on a new task drawn from the unknown task distribution. In this paper, focusing on fixed…
Adiabatic distorted-wave approximation (ADWA) study of three-body $(d,p)$ transfer reactions [G.W. Bailey, N.K. Timofeyuk, and J.A. Tostevin, Phys. Rev. Lett. 117, 162502 (2016)] reported strong sensitivity of cross sections to the…
In computational inverse problems, it is common that a detailed and accurate forward model is approximated by a computationally less challenging substitute. The model reduction may be necessary to meet constraints in computing time when…
Nuclear density functional theory is the prevalent theoretical framework for accurately describing nuclear properties at the scale of the entire chart of nuclides. Given an energy functional and a many-body scheme (e.g., single- or…
We derive information-theoretic lower bounds on the Bayes risk and generalization error of realizable machine learning models. In particular, we employ an analysis in which the rate-distortion function of the model parameters bounds the…
Bayesian neural networks (BNNs) have recently regained a significant amount of attention in the deep learning community due to the development of scalable approximate Bayesian inference techniques. There are several advantages of using a…
In this work, we investigate the value of uncertainty modeling in 3D super-resolution with convolutional neural networks (CNNs). Deep learning has shown success in a plethora of medical image transformation problems, such as…
Any discovery of `new physics' in the neutrino sector first requires a precise prediction of the expected Standard Model cross section. Currently, Coherent Elastic neutrino-Nucleus Scattering (CEvNS) experiments are statistics limited.…
The extraction of any physical information from quasielastic neutron scattering spectra is generally done by fitting a model to the data by means of chi-square minimization procedure. However, as pointed out by the pioneering work of D.S.…