Related papers: IKEBANA: A Neural-Network approach for the K-shell…

This paper reports on the use of a convolutional neural network (CNN) methodology to analyse fluorescence images of calcium-ion Coulomb crystals in the gas phase. A transfer-learning approach is adopted using the publicly available RESNET50…

Within the framework of the second-order non-relativistic quantum perturbation theory, the methods of the theory of irreducible tensor operators, and nonorthogonal orbitals, the absolute values and shape of the generalized cross-sections of…

At impact energies $ \stackrel{>}{\sim}1$ GeV/u the projectile-electron excitation and loss occurring in collisions between highly charged ions and neutral atoms is already strongly influenced by the presence of atomic electrons. In order…

Electron Capture (EC) cross-section from K, L and M shells of the target atoms to the vacant K, L and M shells of the projectile ions have been calculated by deriving the accurate momentum transfer to the captured electrons for different…

We consider the problem of deep neural net compression by quantization: given a large, reference net, we want to quantize its real-valued weights using a codebook with $K$ entries so that the training loss of the quantized net is minimal.…

The interaction of neural networks with physical equations offers a wide range of applications. We provide a method which enables a neural network to transform objects subject to given physical constraints. Therefore an U-Net architecture…

A quantitative understanding of the weak nuclear response is a prerequisite for the analyses of neutrino experiments such as K2K and MiniBOONE, which measure energy and angle of the muons produced in neutrino-nucleus interactions in the…

The availability of the double-differential charged-current neutrino cross section, measured by the MiniBooNE collaboration using a carbon target, allows for a systematic comparison of nuclear effects in quasi-elastic electron and neutrino…

We reexamine the role of electron binding effects in the inelastic neutrino-atom scattering induced by the neutrino magnetic moment. The differential cross section of the process is presented as a sum of the longitudinal and transverse…

We calculate the efficiency of iron K line emission and iron K absorption in photoionized models using a new set of atomic data. These data are more comprehensive than those previously applied to the modeling of iron K lines from…

This paper addresses the use of neural networks for the estimation of treatment effects from observational data. Generally, estimation proceeds in two stages. First, we fit models for the expected outcome and the probability of treatment…

I review the main features of the nuclear response extracted from electron scattering data. The emerging picture clearly shows that the shell model does not provide a fully quantitative description of nuclear dynamics. On the other hand,…

This study introduces the concept of finite element network analysis (FENA) which is a physics-informed, machine-learning-based, computational framework for the simulation of complex physical systems. The framework leverages the extreme…

We propose a simple, but efficient and accurate machine learning (ML) model for developing high-dimensional potential energy surface. This so-called embedded atom neural network (EANN) approach is inspired by the well-known empirical…

We extend a recently developed three-dimensional semiclassical model to study double and triple ionization of Ne driven by infrared laser pulses at various intensities. This model fully accounts for the Coulomb singularity of each electron…

Total cross sections for single ionization and excitation of molecular hydrogen by antiproton impact are presented over a wide range of impact energy from 1 keV to 6.5 MeV. A nonpertubative time-dependent close-coupling method is applied to…

We present high resolution laboratory spectra of K-shell X-ray lines from inner-shell excited and ionized ions of oxygen, obtained with a reflection grating spectrometer on the electron beam ion trap (EBIT-I) at the Lawrence Livermore…

Toxicity prediction of chemical compounds is a grand challenge. Lately, it achieved significant progress in accuracy but using a huge set of features, implementing a complex blackbox technique such as a deep neural network, and exploiting…

We use machine learning techniques to solve the nuclear two-body bound state problem, the deuteron. We use a minimal one-layer, feed-forward neural network to represent the deuteron S- and D-state wavefunction in momentum space, and solve…

Theoretical simulation is helpful for accurate interpretation of experimental X-ray absorption near-edge structure (XANES) spectra that contain rich atomic and electronic structure information of materials. However, current simulation…