Add to ORKGAbstract: An accurate impact parameter determination in a heavy ion collision is crucial for almost all further analysis. The capabilities of an artificial neural network are investigated to that respect. A novel input generation for the network is proposed, namely the transverse and longitudinal momentum distribution of all outgoing (or actually detectable) particles. The neural network approach yields an improvement in performance of a factor of two as compared to classical techniques. To achieve this improvement simple network architectures and a 5 × 5 input grid in (pt, pz) space are suffcient
This thesis describes the application of an Artificial Neural Network classifier to identify the par...
A deep convolutional neural network (CNN) is developed to study symmetry energy (Esym(ρ)) effects by...
We demonstrate high prediction accuracy of three important properties that determine the initial geo...
Accurate impact parameter determination in a heavy-ion collision is crucial for almost all further a...
An accurate impact parameter determination in a heavy ion collision is crucial for almost all furthe...
In this study, we employed a two-stage backpropagation neural network (NNW) to estimate the impact ...
In this study, Au+Au collisions with the impact parameter of $0 \leq b \leq 12.5$ fm at $\sqrt{s_{NN...
We use artificial neural networks (ANNs) to study proton impact single ionization double differentia...
Impact parameter is an important quantity which characterizes the centrality in nucleus-nucleus coll...
The deep learning technique has been applied for the first time to investigate the possibility of ce...
We train a deep convolutional neural network to predict hydrodynamic results for flow coefficients, ...
Using deep convolutional neural network (CNN), the nature of the QCD transition can be identified fr...
Neural networks provide an alternative approach for the solution of complex non-linear data fitting ...
Over the last years, machine learning tools have been successfully applied to a wealth of problems i...
A new method of event characterization based on Deep Learning is presented. The PointNet models can ...
This thesis describes the application of an Artificial Neural Network classifier to identify the par...
A deep convolutional neural network (CNN) is developed to study symmetry energy (Esym(ρ)) effects by...
We demonstrate high prediction accuracy of three important properties that determine the initial geo...
Accurate impact parameter determination in a heavy-ion collision is crucial for almost all further a...
An accurate impact parameter determination in a heavy ion collision is crucial for almost all furthe...
In this study, we employed a two-stage backpropagation neural network (NNW) to estimate the impact ...
In this study, Au+Au collisions with the impact parameter of $0 \leq b \leq 12.5$ fm at $\sqrt{s_{NN...
We use artificial neural networks (ANNs) to study proton impact single ionization double differentia...
Impact parameter is an important quantity which characterizes the centrality in nucleus-nucleus coll...
The deep learning technique has been applied for the first time to investigate the possibility of ce...
We train a deep convolutional neural network to predict hydrodynamic results for flow coefficients, ...
Using deep convolutional neural network (CNN), the nature of the QCD transition can be identified fr...
Neural networks provide an alternative approach for the solution of complex non-linear data fitting ...
Over the last years, machine learning tools have been successfully applied to a wealth of problems i...
A new method of event characterization based on Deep Learning is presented. The PointNet models can ...
This thesis describes the application of an Artificial Neural Network classifier to identify the par...
A deep convolutional neural network (CNN) is developed to study symmetry energy (Esym(ρ)) effects by...
We demonstrate high prediction accuracy of three important properties that determine the initial geo...