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Employing the neural network framework, we obtain empirical fits to the electron-scattering cross sections for carbon over a broad kinematic region, extending from the quasielastic peak through resonance excitation to the onset of…
We investigate whether a neural network approach can reproduce and predict the electron-nucleus cross sections in the kinematical domain of present and future accelerator-based neutrino oscillation experiments. For this purpose, we consider…
We introduce a method to reconstruct the kinematics of neutral-current deep inelastic scattering (DIS) using a deep neural network (DNN). Unlike traditional methods, it exploits the full kinematic information of both the scattered electron…
We review experimental and theoretical cross sections for electron scattering in nitric oxide (NO) and form a comprehensive set of plausible cross sections. To assess the accuracy and self-consistency of our set, we also review electron…
Deep neural networks provide flexible frameworks for learning data representations and functions relating data to other properties and are often claimed to achieve 'super-human' performance in inferring relationships between input data and…
We study the use of deep learning techniques to reconstruct the kinematics of the neutral current deep inelastic scattering (DIS) process in electron-proton collisions. In particular, we use simulated data from the ZEUS experiment at the…
Highly inelastic electron scattering is analyzed within the context of the unified relativistic approach previously considered in the case of quasielastic kinematics. Inelastic relativistic Fermi gas modeling that includes the complete…
Theoretical predictions for elastic neutrino-electron scattering have no hadronic or nuclear uncertainties at leading order making this process an important tool for normalizing neutrino flux. However, the process is subject to large…
We construct a model for inelastic neutrino- and electron-nucleon scattering cross sections using effective leading order parton distribution functions with a new scaling variable $\xi_w$. Non-perturbative effects are well described using…
Multiple scattering and attenuation corrections in Deep Inelastic Neutron Scattering experiments are analyzed. The theoretical basis is stated, and a Monte Carlo procedure to perform the calculation is presented. The results are compared…
With the rise of the popularity and usage of neural networks, trustworthy uncertainty estimation is becoming increasingly essential. One of the most prominent uncertainty estimation methods is Deep Ensembles (Lakshminarayanan et al., 2017)…
We present a unified relativistic approach to inclusive electron scattering based on the relativistic Fermi gas model and on a phenomenological extension of it which accounts for the superscaling behaviour of $(e,e')$ data. We present…
Deep-learning-based data-driven forecasting methods have produced impressive results for traffic forecasting. A major limitation of these methods, however, is that they provide forecasts without estimates of uncertainty, which are critical…
High precision studies of Beyond-Standard-Model physics through accelerator-based neutrino oscillation experiments require a very accurate description of neutrino-nucleus cross sections in a broad energy region, going from quasielastic…
Understanding nuclear effects is essential for improving the sensitivity of neutrino oscillation measurements. Validating nuclear models solely through neutrino scattering data is challenging due to limited statistics and the broad energy…
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…
Deep learning applications at the network edge lead to a significant growth in AI-related carbon emissions, presenting a critical sustainability challenge. The existing edge computing frameworks optimize for latency and throughput, but they…
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…
Long-lead forecasting for spatio-temporal systems can often entail complex nonlinear dynamics that are difficult to specify it a priori. Current statistical methodologies for modeling these processes are often highly parameterized and thus,…
We present an improved model on neutrino- and electron-nucleon scattering cross sections using effective leading order PDFs with a new scaling variable xi_w. Non-perturbative QCD effects at low Q^2 are separately treated for u and d valence…