相关论文: Improving nuclear data evaluations with predictive…
Nuclear physics offers us a powerful tool: using nuclear resonance absorption lines to infer the physical conditions in astrophysical settings which are otherwise difficult to deduce. Present-day technology provides an increase in…
Decision-making in manufacturing often involves optimizing key process parameters using data collected from simulation experiments. Gaussian processes are widely used to surrogate the underlying system and guide optimization. Uncertainty…
We suggest that superscaling in electroweak interactions with nuclei, namely the observation that the reduced electron-nucleus cross sections are to a large degree independent of the momentum transfer and of the nuclear species, can be used…
Neural networks can be used as surrogates for PDE models. They can be made physics-aware by penalizing underlying equations or the conservation of physical properties in the loss function during training. Current approaches allow to…
The sources of nuclear uncertainties in nova nucleosynthesis have been identified using hydrodynamical nova models. Experimental efforts have followed and significantly reduced those uncertainties. This is important for the evaluation of…
The determination of the injection composition of cosmic ray nuclei within astrophysical sources requires sufficiently accurate descriptions of the source physics and the propagation - apart from controlling astrophysical uncertainties. We…
The composition of the space radiation environment inside spacecrafts is modified by the interaction with shielding material, with equipment and even with the astronauts' bodies. Accurate quantitative estimates of the effects of nuclear…
Modern X-ray spectroscopy has proven itself as a robust tool for probing the electronic structure of atoms in complex environments. Despite working on energy scales that are much larger than those corresponding to nuclear motions, taking…
A quantitative understanding of the weak nuclear response is a prerequisite for the computer simulations of astrophysical phenomena like supernov$\ae$ explosions and neutron star cooling. In order to reduce the systematic uncertainties…
Physics models typically contain adjustable parameters to reproduce measured data. While some parameters correspond directly to measured features in the data, others are unobservable. These unobservables can, in some cases, cause…
Being able to rigorously quantify the uncertainties in reaction models is crucial to moving this field forward. Even though Bayesian methods are becoming increasingly popular in nuclear theory, they are yet to be implemented and applied in…
Neural networks for LHC physics have to be accurate, reliable, and controlled. Using neural surrogates for the prediction of loop amplitudes as a use case, we first show how activation functions are systematically tested with…
Hypothesis testing based on surrogate data has emerged as a popular way to test the null hypothesis that a signal is a realization of a linear stochastic process. Typically, this is done by generating surrogates which are made to conform to…
Power-law distributions are essential in computational and statistical investigations of extreme events and complex systems. The usual technique to generate power-law distributed data is to first infer the scale exponent $\alpha$ using the…
Modeling is crucial to understanding the effect of greenhouse gases, warming, and ice sheet melting on the ocean. At the same time, ocean processes affect phenomena such as hurricanes and droughts. Parameters in the models that cannot be…
We present a probabilistic deep learning methodology that enables the construction of predictive data-driven surrogates for stochastic systems. Leveraging recent advances in variational inference with implicit distributions, we put forth a…
An overview of the experimental aspects of nucleon transfer reactions with radioactive beams is presented, aimed principally at a researcher who is beginning their work in this area. Whilst the physics motivation and the means of…
Reactions with radioactive nuclear beams at relativistic energies have opened new doors to clarify the mechanisms of stellar evolution and cataclysmic events involving stars and during the big bang epoch. Numerous nuclear reactions of…
The treatment of nuclear effects in neutrino-nucleus interactions is one of the main sources of systematic uncertainty for the analysis and interpretation of data of neutrino oscillation experiments. Neutrinos interact with nuclei via…
Atomic radii and charges are two major parameters used in implicit solvent electrostatics and energy calculations. The optimization problem for charges and radii is under-determined, leading to uncertainty in the values of these parameters…