A Neural Network Approach for Orienting Heavy-Ion Collision Events

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 $E_{\text{beam}}=1\text{GeV/nucleon}$. 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 (25)

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Nuclear ground-state masses and deformations: FRDM(2012)
• 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
• Neutron drip line in the Ca region from Bayesian model averaging
• Nuclear charge radii: Density functional theory meets Bayesian neural networks
• Nuclear mass systematics using neural networks
• Taming nuclear complexity with a committee of multilayer neural networks
• Long-range azimuthal correlations in proton-proton and proton-nucleus collisions from the incoherent scattering of partons
• Neural Networks for Impact Parameter Determination
• Lambda polarization in ${}^{108}$Ag +${}^{108}$Ag and ${}^{197}$Au +${}^{197}$Au collisions around a few GeV
• Experimental study of fragmentation products in the reactions 112Sn + 112Sn and 124Sn + 124Sn at 1 AGeV
• Examination of cluster production in excited light systems at Fermi energies from new experimental data and comparison with transport model calculations
• Novel method for producing very-neutron-rich hypernuclei via charge-exchange reactions with heavy ion projectiles
• Empirical physical formula for potential energy curves of 38-66Ti isotopes by using neural networks
• Bayesian inference of in-medium baryon-baryon scattering cross sections from HADES proton flow data
• A Kohn-Sham Scheme Based Neural Network for Nuclear Systems
• Microscopic study of the deformed neutron halo of 31Ne
• Impact of quadrupole deformation on intermediate-energy heavy-ion collisions
• Initialization effects of nucleon profile on the $\pi$ yields in heavy-ion collisions at medium energies
• Classification of Equation of State in Relativistic Heavy-Ion Collisions Using Deep Learning
• 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
• Dependence on beam energy and nuclear equation of state dependence of anisotropic flow and particle production in low-energy heavy-ion collisions
• Any Deep ReLU Network is Shallow

Cited by in corpus (1)

• Neural Network Emulation of Flow in Heavy-Ion Collisions at Intermediate Energies