相关论文: Determination of impact parameter for CEE with dig…
A new method of event characterization based on Deep Learning is presented. The PointNet models can be used for fast, online event-by-event impact parameter determination at the CBM experiment. For this study, UrQMD and the CBM detector…
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…
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 proton-nucleus and nucleus-nucleus collision experiments, one determines the centrality of a collision according to the multiplicity or energy deposited in a detector. This serves as a proxy for the true collision centrality, as defined…
Machine Learning (ML) algorithms have been demonstrated to be capable of predicting impact parameter in heavy-ion collisions from transport model simulation events with perfect detector response. We extend the scope of ML application to…
Background: $^{132}$Sn+$^{124}$Sn collisions at the beam energy of 270 MeV$/$nucleon have been performed at the Radioactive Isotope Beam Factory (RIBF) in RIKEN to investigate the nuclear equation of state. Reconstructing impact parameter…
The impact parameter is one of the crucial physical quantities of heavy-ion collisions (HICs), and can affect obviously many observables at the final state, such as the multifragmentation and the collective flow. Usually, it cannot be…
The determination of centrality in nucleus-nucleus collisions is a crucial task, as it enables the estimation of the impact parameter and thereby allows for the comparison of experimental results with predictions from theoretical models and…
To enable an accurate determination of oscillation parameters, accelerator-based neutrino experiments require detailed simulations of nuclear interaction physics in the GeV regime. While substantial effort from both theory and experiment is…
The nucleus-nucleus impact parameter and collision geometry of a heavy ion collision are typically characterized by assigning a collision "centrality". In all present heavy ion experiments centrality is measured indirectly, by detecting the…
A deep convolutional neural network (CNN) is developed to study symmetry energy $E_{\rm sym}(\rho)$ effects by learning the mapping between the symmetry energy and the two-dimensional (transverse momentum and rapidity) distributions of…
We demonstrate that the nuclear collision geometry (i.e. impact parameter) can be determined with 1.5 fm accuracy in an event-by-event analysis by measuring the transverse energy flow in the pseudorapidity region $3 \le |\eta| \le 5$ with a…
We improve the centrality definition in impact parameter in PACIAE model responding the fact reported by the ALICE, ATLAS, and CMS collaborations that the maximum impact parameter in heavy ion collisions should be extended to 20 $fm$.…
In ultrarelativistic heavy-ion experiments, one estimates the centrality of a collision by using a single observable, say $n$, typically given by the transverse energy or the number of tracks observed in a dedicated detector. The…
In neutrino oscillation experiments, neutrino interactions at the detector are simulated using event generators which attempt to reflect our understanding of nuclear physics. We study the impact of different neutrino interactions and…
While current nuclear parton distribution functions (nPDFs) from global fits to experimental data are spatially homogeneous, many experimental observables in nucleus-nucleus collisions are presented in terms of centrality cuts. These cuts…
Photographic Nuclear Emulsion Detector (PNED) has been in use in nuclear and particle physics experiments from the begining, often as the major detector system. However, direct measurement of impact parameter in this detector does not seem…
Rapidly shrinking technology node and voltage scaling increase the susceptibility of Soft Errors in digital circuits. Soft Errors are radiation-induced effects while the radiation particles such as Alpha, Neutrons or Heavy Ions, interact…
Centrality, as a geometrical property of the collision, is crucial for the physical interpretation of nucleus-nucleus and proton-nucleus experimental data. However, it cannot be directly accessed in event-by-event data analysis. Common…
This work applies concepts of artificial neural networks to identify the parameters of a mathematical model based on phase fields for damage and fracture. Damage mechanics is the part of the continuum mechanics that models the effects of…