Nuclear liquid-gas phase transition with machine learning
Rui Wang, Yu-Gang Ma, R. Wada, Lie-Wen Chen, Wan-Bing He, Huan-Ling Liu, Kai-Jia Sun
Abstract
The authors employ machine learning techniques to identify nuclear liquid-gas phase transition in heavy-ion experiment and determine its limiting temperature, directly from the experimental final state charged particles' multiplicity distribution.
Topics & Concepts
Artificial intelligenceComputer sciencePhase transitionLimitingMachine learningPhysicsState (computer science)Phase (matter)Transition (genetics)Statistical physicsMultiplicity (mathematics)Stability (learning theory)EngineeringAlgorithmMachine Learning in Materials ScienceNuclear physics research studiesHigh-Energy Particle Collisions Research