Related papers: A Novel Deep Learning Method for Detecting Nucleon…
We propose a new strategy to probe heavy neutrinos with non-universal fermion couplings at the Large Hadron Collider (LHC) using a novel production mechanism and machine-learning algorithms. Focusing on proton--proton collisions at…
Employing the isospin-dependent Boltzmann-Uehling-Uhlenbeck transport model, the 1 GeV/nucleon deformed uranium-uranium ultra-central collisions are simulated. Based on sensitive observables, mean square collective flow and pion meson…
The experiments at the Large Hadron Collider at CERN generate vast amounts of complex data from high-energy particle collisions. This data presents significant challenges due to its volume and complex reconstruction, necessitating the use…
The methods currently used to measure azimuthal distributions of particles in heavy ion collisions assume that all azimuthal correlations between particles result from their correlation with the reaction plane. However, other correlations…
At the extreme energies of the Large Hadron Collider, massive particles can be produced at such high velocities that their hadronic decays are collimated and the resulting jets overlap. Deducing whether the substructure of an observed jet…
Node classification in structural networks has been proven to be useful in many real world applications. With the development of network embedding, the performance of node classification has been greatly improved. However, nearly all the…
We consider the impact of the recent data obtained by the LHC, Tevatron, and fixed-target experiments on the nucleon quark distributions with a particular focus on disentangling different quark species. An improved determination of the…
Fast-pairwise neutrino oscillations potentially affect many aspects of core-collapse supernova (CCSN): the explosion mechanism, neutrino signals, and nucleosynthesis in the ejecta. This particular mode of collective neutrino oscillations…
Symmetry-plane correlations (SPCs) are key observables sensitive to the medium's transport properties and are driven by participant-plane correlations (PPCs) in the nuclear overlap region. This study explores the possibility of…
An important part of the physics program at the future electron-ion collider is to understand the nature of hadronization and the transport of energy and matter in large nuclei. Open heavy flavor production in deep inelastic scattering…
Study of the characteristic properties of charged particle production in hadron-nucleus collisions at high energies, by utilising the approaches from different statistical models is performed.~Predictions from different approaches using the…
In this work, we explore the use of deep learning techniques to learn how nuclear cross sections change as we add or remove protons and neutrons. As a proof of principle, we focus on the neutron-induced reactions in the fast energy regime.…
We use a Glauber-like approach to describe very energetic nucleus-nucleus collisions as a sequence of binary nucleon-nucleon collisions. No free parameters are needed: all the information comes from simple parametrizations of…
Semi-inclusive deep inelastic lepton scattering off few-nucleon systems is investigated assuming that virtual boson absorption occurs on a hadronic cluster which can be either a two-nucleon correlated pair or a six-quark bag. In both cases…
The microscopic phasespace approach URQMD is used to investigate the stopping power and particle production in heavy systems at SPS and RHIC energies. This framework bridges with one model consistently the entire available range of energies…
Accurate and timely prediction of crash severity is crucial in mitigating the severe consequences of traffic accidents. Accurate and timely prediction of crash severity is crucial in mitigating the severe consequences of traffic accidents.…
Neutrinos play a crucial role in the core-collapse supernova (CCSN) explosion mechanism. The requirement of accurately calculating the transport of neutrinos makes simulations of the CCSN mechanism extremely challenging and computationally…
HIJING generator simulation of nucleus-nucleus collisions at ultrarelativistic energies are presented. Is shown, that the global characteristics of nucleus-nucleus collisions, such as of distribution of a charge multiplicity, total and…
Deep learning techniques are currently being investigated for high energy physics experiments, to tackle a wide range of problems, with quark and gluon discrimination becoming a benchmark for new algorithms. One weakness is the traditional…
High-energy nuclear collisions encompass three key stages: the structure of the colliding nuclei informed by low-energy nuclear physics, the initial condition (IC) leading to the formation of quark-gluon plasma (QGP), and the hydrodynamic…