A Neural Network Approach for Orienting Heavy-Ion Collision Events
arXiv:2308.15796 · doi:10.1016/j.physletb.2023.138359
Abstract
A convolutional neural network-based classifier is elaborated to retrace the initial orientation of deformed nucleus-nucleus collisions by integrating multiple typical experimental observables. The isospin-dependent Boltzmann-Uehling-Uhlenbeck transport model is employed to generate data for random orientations of ultra-central uranium-uranium collisions at . Statistically, the data-driven polarization scheme is essentially accomplished via the classifier, whose distinct categories filter out specific orientation-biased collision events. This will advance the deformed nucleus-based studies on nuclear symmetry energy, neutron skin, etc.
References in corpus (13)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- Nuclear mass predictions based on Bayesian neural network approach with pairing and shell effects
- Elliptic and triangular flow in p+Pb and peripheral Pb+Pb collisions from parton scatterings
- Nuclear charge radii: Density functional theory meets Bayesian neural networks
- Long-range azimuthal correlations in proton-proton and proton-nucleus collisions from the incoherent scattering of partons
- Examination of cluster production in excited light systems at Fermi energies from new experimental data and comparison with transport model calculations
- Microscopic study of the deformed neutron halo of 31Ne
- A Kohn-Sham Scheme Based Neural Network for Nuclear Systems
- Bayesian inference of in-medium baryon-baryon scattering cross sections from HADES proton flow data
- Impact of quadrupole deformation on intermediate-energy heavy-ion collisions
- A local-density-approximation description of high-momentum tails in isospin asymmetric nuclei
- Principal Component Analysis of Azimuthal Flow in Intermediate-Energy Heavy-Ion Reactions
- Any Deep ReLU Network is Shallow