Fupeng LI, Longgang PANG, Xinnian WANG. Application of machine learning to the study of QCD transition in heavy ion collisions[J]. NUCLEAR TECHNIQUES, 2023, 46(4): 040014
- NUCLEAR TECHNIQUES
- Vol. 46, Issue 4, 040014 (2023)
![Demonstration of difficulty encountered when extracting the nuclear equation of state (EoS) through model-data comparison in heavy-ion collisions[4]](/richHtml/hjs/2023/46/4/040014/040014-F001.jpg)
Fig. 1. Demonstration of difficulty encountered when extracting the nuclear equation of state (EoS) through model-data comparison in heavy-ion collisions[4]
Fig. 2. Demonstration of the sign problem in lattice QCD
![Extraction of nuclear EoS at a high temperature and zero baryon chemical potential region using Bayesian analysis[19]](/Images/icon/loading.gif){kind=icon}
Fig. 3. Extraction of nuclear EoS at a high temperature and zero baryon chemical potential region using Bayesian analysis[19]
Fig. 4. A single layer artificial neural network transforms the input data by a linear matrix operation z=xW+b, in combination with a non-linear operation on each element of the output z using a non-linear activation function
Fig. 5. Identification of the nuclear EoS and types of phase transition from the final state particle spectra using deep convolutional neural network[24]
Fig. 6. Identification of the nuclear EoS from the final state particle cloud in momentum space using point cloud neural network[28]
Fig. 7. Search for self-similarity in momentum space using dynamical edge convolution network and identification of correlated particles[39]
Fig. 8. Framework of the deep neural network when constructing three temperature-dependent mass functions and calculating the QCD EoS using DNN learned masses[42]
Fig. 9. Flow chart illustrating active learning[43]
Fig. 10. Classification of nuclear liquid gas phase transition using an auto encoder, which learns from the experimental data of heavy-ion collisions[46]
Fig. 12. Deep learning assisted jet tomography used to locate the initial jet production positions and aid in searching the Mach cone in nuclear liquid[58]
Fig. 13. Flow chart illustrating active learning[61]
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Table 1. Comparison between the performances of dynamical edge convolution network and point cloud network when searching for critical phenomenon
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