Related papers: Neural Network based Electron Identification in th…
Laser-based ion acceleration is driven by electrical fields emerging when target electrons absorb laser energy and consecutively leave the target material. A direct correlation between these electrons and the accelerated ions is thus to be…
The MUonE experiment proposes a novel approach to determine the leading hadronic contribution to the muon g-2, from a precise measurement of the differential cross section of the $\mu-e$ elastic scattering, achievable by using the CERN SPS…
We develop a combined theoretical and experimental method for estimating the amount of heating that occurs in metallic nanoparticles that are being imaged in an electron microscope. We model the thermal transport between the nanoparticle…
We present a quantum optics-based detection method for determining the position and current of an electron beam. As electrons pass through a dilute vapor of rubidium atoms, their magnetic field perturb the atomic spin's quantum state and…
The efficacy of particle identification is compared using artificial neutral networks and boosted decision trees. The comparison is performed in the context of the MiniBooNE, an experiment at Fermilab searching for neutrino oscillations.…
A computer program is presented which calculates the elastic and inelastic scattering in intermediate and high energy nuclear collisions. A coupled-channels method is used for Coulomb and nuclear excitations of E1, E2, E3, M1, and M2…
In nuclear fuel cycles, an enrichment facility typically provides low enriched uranium (LEU) to a number of customers. We consider monitoring an enrichment facility to timely detect a possible diversion of highly enriched uranium (HEU). To…
A deep learning based method with the convolutional neural network (CNN) algorithm for determining the impact parameters is developed using the constrained molecular dynamics model simulations, focusing on the heavy-ion collisions at the…
In this work, we present results for discrimination of neutron and $\gamma$ events using a plastic scintillator detector with pulse shape discrimination capabilities. Machine learning (ML) algorithms are used to improve the discriminatory…
This paper presents a data processing algorithm with machine learning for polarization extraction and event selection applied to photoelectron track images taken with X-ray polarimeters. The method uses a convolutional neural network (CNN)…
This work presents the application of the artificial neural networks, trained and structurally optimized by genetic algorithms, for modeling of crude distillation process at PKN ORLEN S.A. refinery. Models for the main fractionator…
The detection of nuclear spins using individual electron spins has enabled new opportunities in quantum sensing and quantum information processing. Proof-of-principle experiments have demonstrated atomic-scale imaging of nuclear-spin…
In this study, Au+Au collisions with the impact parameter of $0 \leq b \leq 12.5$ fm at $\sqrt{s_{NN}} = 200$ GeV are simulated by the AMPT model to provide the preliminary final-state information. After transforming these information into…
Accurate thermal analysis of composites and porous media requires detailed characterization of local thermal properties in small scale. For some important applications such as lithium-ion batteries, changes in the properties during the…
The EIC will deliver collisions of electrons with protons and nuclei at a wide variety of energies and at luminosities up to 1000 times higher than HERA. Precise measurement of both the scattered electron and the hadronic final state is…
We have studied the performance of a new algorithm for electron/pion separation in an Emulsion Cloud Chamber (ECC) made of lead and nuclear emulsion films. The software for separation consists of two parts: a shower reconstruction algorithm…
We describe the design concept and estimated performance of an iron-scintillator sampling calorimeter for the future Electron Ion Collider. The novel aspect of this detector is a multi-dimensional readout coupled with foreseen excellent…
We develop a neural network model, based on the processes of high-energy heavy-ion collisions, to study and predict several experimental observables in Au+Au collisions. We present a data-driven deep learning framework for predicting…
We developed a convolutional neural network (CNN) model capable of predicting the performance of various ion-doped NASICON compounds by leveraging extensive datasets from prior experimental investigation.The model demonstrated high accuracy…
A method is presented for inferring the deuterium fuel ion temperature from neutron counts measured with fast liquid scintillators in conditions where the ion velocity distribution is Maxwellian. Local neutron count rates at each…