An equation-of-state-meter of quantum chromodynamics transition from deep learning
The large data generated in heavy-ion collision experiments require careful analysis to understand the physics. Here the authors use the deep-learning method to sort equation of states in QCD transition and analyze the simulated data sets mimicking the heavy-ion collision experiments.
Guardado en:
Autores principales: | Long-Gang Pang, Kai Zhou, Nan Su, Hannah Petersen, Horst Stöcker, Xin-Nian Wang |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2018
|
Materias: | |
Acceso en línea: | https://doaj.org/article/1ca7b75f4eb446d4a968181d0b0fb0a1 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Automatic Detection of Display Defects for Smart Meters based on Deep Learning
por: Ye Chen, et al.
Publicado: (2020) -
Using a quantum work meter to test non-equilibrium fluctuation theorems
por: Federico Cerisola, et al.
Publicado: (2017) -
Phase transition in the cuprates from a magnetic-field-free stiffness meter viewpoint
por: Itzik Kapon, et al.
Publicado: (2019) -
Search for coherent bremsstrahlung from spontaneous fission at 555 meter deep underground laboratory
por: Deepak Pandit, et al.
Publicado: (2021) -
A Deep Reinforcement Learning Approach for Ramp Metering Based on Traffic Video Data
por: Bing Liu, et al.
Publicado: (2021)