Determining impact parameters of heavy-ion collisions at low-intermediate incident energies using deep learning with convolutional neural network

Abstract

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 low-intermediate incident energies from several ten to one hundred MeV/nucleon in which the emissions of heavy fragments with the charge numbers larger than 3 become crucial. To make the CNN applicable in the task of the impact parameter determination at the present energy range, specific improvements are made in the input selection, the CNN construction and the CNN training. It is demonstrated from the comparisons of the deep CNN method and the conventional methods with the impact parameter-sensitive observables, that the deep CNN method shows better performance for determining the impact parameters, especially leading to the capability of providing better recognition of the central collision events. With a proper consideration of the experimental filter effect in both training and testing processes to keep consistency with the actual experiments, the good performance of the deep CNN method holds, and shows significantly better in terms of predicting the impact parameters and recognizing the central collision events, compared to that of the conventional methods, demonstrating the superiority of the present deep CNN method. The deep CNN method with the consideration of the filter effect is applied in the deduction of nuclear stopping power. Higher accuracy for the stopping power deduction is achieved benefitting from the better impact parameter determination using the deep CNN method, compared to using the the conventional methods. This result reveals the importance to select a reliable impact parameter determination method in the experimental deduction of the nuclear stopping power as well as other observables.

arXiv comment21 pages, 16 figures, 1 table

References in corpus (36)

• Machine learning and the physical sciences
• Neutron Star Structure and the Equation of State
• Determination of the Equation of State of Dense Matter
• Isotopic Scaling in Nuclear Reactions
• Isospin Fractionation in Nuclear Multifragmentation
• The equation of state for dense nucleonic matter from a metamodeling. I. Foundational aspects
• Constraint Molecular Dynamics approach to Fermionic systems
• The Giant Dipole Resonance as a quantitative constraint on the symmetry energy
• A systematic description of evaporation spectra for light and heavy compound nuclei
• Isotopic dependence of the giant monopole resonance in the even-A ^{112-124}Sn isotopes and the asymmetry term in nuclear incompressibility
• Neutron and Proton Transverse Emission Ratio Measurements and the Density Dependence of the Asymmetry Term of the Nuclear Equation of State
• Nuclear multifragmentation and phase transition for hot nuclei
• An Improved Quantum Molecular Dynamics Model and its Applications to Fusion Reaction near Barrier
• Central Collisions of Au on Au at 150, 250 and 400 A MeV
• Understanding transport simulations of heavy-ion collisions at 100 and 400 AMeV: Comparison of heavy ion transport codes under controlled conditions
• Comparison of heavy-ion transport simulations: Collision integral in a box
• Constrained Molecular Dynamics II: a N-body approach to nuclear systems
• Dynamics of clusters and fragments in heavy-ion collisions
• Reaction Dynamics and Multifragmentation in Fermi Energy Heavy Ion Reactions
• Comparison of Heavy-Ion Transport Simulations: Mean-field Dynamics in a Box
• In-medium effects for nuclear matter in the Fermi energy domain
• Symmetry energy investigation with pion production from Sn+Sn systems
• Mass and charge identification of fragments detected with the Chimera Silicon-CsI(Tl) telescopes
• Unified Description of Nuclear Stopping in Central Heavy-ion Collision from 10$A$ MeV to 1.2$A$ GeV
• Multifragmentation in Xe(50A MeV)+Sn Confrontation of theory and data
• Neural Networks for Impact Parameter Determination
• Nuclear liquid-gas phase transition with machine learning
• A novel approach to Isoscaling: the role of the order parameter m = (N-Z)/A
• Statistical Multifragmentation of Non-Spherical Expanding Sources in Central Heavy-Ion Collisions
• Determining the temperature in heavy-ion collisions with multiplicity distribution
• Application of machine learning in the determination of impact parameter in the ${}^{132}$Sn+${}^{124}$Sn system
• Density and Temperature of Bosons from Quantum Fluctuations
• Application of artificial intelligence in the determination of impact parameter in heavy-ion collisions at intermediate energies
• Providing physics guides in Bayesian neural networks from input layer: case of giant dipole resonance predictions
• Estimation of Impact Parameter and Transverse Spherocity in heavy-ion collisions at the LHC energies using Machine Learning
• Impact parameter smearing effects on isospin sensitive observables in heavy ion collisions

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