Related papers: Empirical fits to inclusive electron-carbon scatte…

We developed a novel approach based on a generalization of factorization and nuclear spectral functions, allowing for a consistent treatment of the amplitudes involving one- and two-nucleon currents, whose contribution to the nuclear…

The calibration of solid constitutive models with full-field experimental data is a long-standing challenge, especially in materials which undergo large deformation. In this paper, we propose a physics-informed deep-learning framework for…

The inclusive electromagnetic responses in the quasi-elastic region are calculated with a model which considers the terms of the cluster expansion containinga single correlation line. The validity of this model is studied by comparing, in…

We propose a new adequacy test and a graphical evaluation tool for nonlinear dynamic models. The proposed techniques can be applied in any setup where parametric conditional distribution of the data is specified, in particular to models…

Reliable uncertainty quantification in deep neural networks is very crucial in safety-critical applications such as automated driving for trustworthy and informed decision-making. Assessing the quality of uncertainty estimates is…

CEBAF experiment e89-008 measured inclusive electron scattering from nuclei in a Q^2 range between 0.8 and 7.3 (GeV/c)^2 for x_{Bjorken} \gtorder 1. The cross sections for scattering from D, C, Fe, and Au were measured. The C, Fe, and Au…

We present a continuum random phase approximation approach to study electron- and neutrino-nucleus scattering cross sections, in the kinematic region where quasielastic scattering is the dominant process. We show the validity of the…

Deep learning has emerged as a technique of choice for rapid feature extraction across imaging disciplines, allowing rapid conversion of the data streams to spatial or spatiotemporal arrays of features of interest. However, applications of…

Results of inclusive measurements of inelastic electron and positron scattering from unpolarized protons and deuterons at the HERMES experiment are presented. The structure functions $F_2^p$ and $F_2^d$ are determined using a…

Changepoint detection is commonly formulated by minimizing the sum of in-sample losses to quantify the model's overall fit. However, for flexible modeling procedures -- especially those involving high-dimensional parameter spaces or…

At the KATRIN experiment, the electron antineutrino mass is inferred from the shape of the $\beta$-decay spectrum of tritium. Important systematic effects in the Windowless Gaseous Tritium Source (WGTS) of the experiment include the energy…

Although numerous machine learning models exist to detect issues like rolling bearing strain and deformation, typically caused by improper mounting, overloading, or poor lubrication, these models often struggle to isolate faults from the…

Safety evaluation of self-driving technologies has been extensively studied. One recent approach uses Monte Carlo based evaluation to estimate the occurrence probabilities of safety-critical events as safety measures. These Monte Carlo…

We present an improved version of the Superscaling Analysis with Relativistic Effective Mass, denoted as SuSAM-v2. In the original SuSAM model, a universal scaling function was fitted to a selected set of quasielastic electron scattering…

We provide a both qualitative and quantitative comparison among different approaches aimed to solve the problem of non-linear diffusive acceleration of particles at shocks. In particular, we show that state-of-the-art models (numerical,…

In this work, we propose a novel deep bootstrap framework for nonparametric regression based on conditional diffusion models. Specifically, we construct a conditional diffusion model to learn the distribution of the response variable given…

Precise neutrino energy reconstruction is essential for next-generation long-baseline oscillation experiments, yet current methods remain limited by large uncertainties in neutrino-nucleus interaction modeling. Even so, it is well…

We suggest a loss for learning deep embeddings. The new loss does not introduce parameters that need to be tuned and results in very good embeddings across a range of datasets and problems. The loss is computed by estimating two…

Electron-impact ionization cross sections of atoms and molecules are essential for plasma modelling. However, experimentally determining the absolute cross sections is not easy, and ab initio calculations become computationally prohibitive…

In this work we devise a theoretical and computational method to compute the elastic scattering of electrons from a non-spherical potential, such as in the case of molecules and molecular aggregates. Its main feature is represented by the…