Related papers: Deeply Learning Deep Inelastic Scattering Kinemati…
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…
Real-time simulation of elastic structures is essential in many applications, from computer-guided surgical interventions to interactive design in mechanical engineering. The Finite Element Method is often used as the numerical method of…
In this paper we present a technique to reconstruct the scaling variables defining $ep$ deep inelastic scattering by performing a kinematic fit. This reconstruction technique makes use of the full potential of the data collected. It is…
We present a detailed theoretical investigation of hadron attenuation in deep inelastic scattering (DIS) off complex nuclei in the kinematic regime of the HERMES experiment. The analysis is carried out in the framework of a probabilistic…
After fifteen years of running and a further five years of analysis, the final inclusive deep inelastic scattering cross sections from H1 and ZEUS have been published. Measurements of neutral current and charged current processes in ep…
In the framework of three-active-neutrino mixing, the charge parity phase, the neutrino mass ordering, and the octant of $\theta_{23}$ remain unknown. The Deep Underground Neutrino Experiment (DUNE) is a next-generation long-baseline…
A method for reconstructing the direction of a fast neutron source using a segmented organic scintillator-based detector and deep learning model is proposed and analyzed. The model is based on recurrent neural network, which can be trained…
Neutral-current and charged-current deep-inelastic scattering at very high four-momentum transfer squared (Q^2) have been studied in positron-proton collisions at center-of-mass energy 300 GeV using the ZEUS detector at HERA. An integrated…
We analyse the newest diffractive deep inelastic scattering data from HERA using the dipole model approach. We find a reasonable good agreement between the predictions and the data although the region of small values of a kinematic variable…
Deeply virtual Compton scattering has recently been studied by three HERA experiments, H1, ZEUS and HERMES, covering a broad range of kinematic regimes. We present cross section measurements of the two collider experiments in the kinematic…
Neutral-current (NC) and charged-current (CC) deep inelastic scattering (DIS) interactions have been studied in electron-proton and positron-proton collisions with longitudinally polarised lepton beams using the H1 and ZEUS detectors at…
Results from the H1 and ZEUS experiments at HERA on deep inelastic scattering are reviewed. The data lead to a consistent picture of a steep rise in the F_2 structure function and in the gluon density within the proton. Important new…
We have developed an image-based convolutional neural network (CNN) that is applicable for quantitative time-resolved measurements of the fragmentation behavior of opaque brittle materials using ultra-high speed optical imaging. This model…
The observation and description of collective excitations in solids is a fundamental issue when seeking to understand the physics of a many-body system. Analysis of these excitations is usually carried out by measuring the dynamical…
We present a deep learning approach for vertex reconstruction of neutrino-nucleus interaction events, a problem in the domain of high energy physics. In this approach, we combine both energy and timing data that are collected in the MINERvA…
Semi-inclusive deep-inelastic scattering (SIDIS) at the Electron-Ion Collider will allow for precise mapping of the 3D momentum and spin structure of nucleons and nuclei over a large kinematic region. In this contribution, we demonstrate…
We review and compare the reconstruction methods of the inclusive deep inelastic scattering variables used at HERA. We introduce a new prescription, the $\Sigma$ method, which allows to measure the structure function of the proton…
In this proceeding, we introduce deep learning technologies for studying hadron-hadron interactions. To extract parameterized hadron interaction potentials from collision experiments, we employ a supervised learning approach using…
This work presents a novel physics-informed deep learning based super-resolution framework to reconstruct high-resolution deformation fields from low-resolution counterparts, obtained from coarse mesh simulations or experiments. We leverage…
Mean values and differential distributions of event-shape variables have been studied in neutral current deep inelastic scattering using an integrated {luminosity} of 82.2 pb$^{-1}$ collected with the ZEUS detector at HERA. The kinematic…