Abstract

The machine-learning techniques have shown their capability for studying phase transitions in condensed matter physics. Here, we employ the machine-learning techniques to study the nuclear liquid-gas phase transition. We adopt an unsupervised learning and classify the liquid and gas phases of nuclei directly from the final state raw experimental data of heavy-ion reactions. Based on a confusion scheme which combines the supervised and unsupervised learning, we obtain the limiting temperature of the nuclear liquid-gas phase transition. Its value $9.24\pm0.04~\rm MeV$ is consistent with that obtained by the traditional caloric curve method. Our study explores the paradigm of combining the machine-learning techniques with heavy-ion experimental data, and it is also instructive for studying the phase transition of other uncontrollable systems, like QCD matter.

Highlights

  • The nuclear liquid-gas phase transition is an old and longlasting topic [1,2,3,4,5,6]

  • The neural network consists of two main parts: the encoder part encodes the inputted event-by-event ZMc(Z ) to a latent variable, and the decoder part decodes the latent variable to ZMc(Z ), and tries to restore the original ZMc(Z )

  • We have shown that machine-learning techniques can be employed to a traditional nuclear physics topic, the nuclear liquid-gas phase transition

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Summary

INTRODUCTION

The nuclear liquid-gas phase transition is an old and longlasting topic [1,2,3,4,5,6]. The nuclear liquid-gas phase transition is realized through tracing the effect of the spinodal instability, which is intimately related to first-order phase transition, on the reaction dynamics, e.g., by measuring the properties of the intermediate mass fragments (with charge number larger than 3). Heavy-ion reaction experiments may bring the excited nuclei into the spinodal region of the phase diagram in which the spinodal instability may develop exponentially and lead to the breakup of nuclei. This is commonly referred to as nuclear multifragmentation. We use the trained networks to classify the liquid and gas phases of nuclei, and determine the limiting temperature of the nuclear liquid-gas phase transition

EXPERIMENTAL DATA
RESULTS
Classifying the liquid and gas phases by the autoencoder method
Limiting temperature from confusion scheme
SUMMARY AND OUTLOOK

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