Determining the temperature in heavy-ion collisions with multiplicity distribution

TL;DR

This paper introduces a neural network-based method to estimate the temperature of heavy-ion collisions using charge multiplicity distributions, providing a new thermometer that detects nuclear phase transitions.

Contribution

It demonstrates that multiplicity distributions can reliably determine collision temperatures, aligning with traditional methods and revealing phase transition signatures.

Findings

Caloric curve indicates nuclear liquid-gas phase transition at ~6.4 MeV.

Neural network effectively relates multiplicity to temperature.

Method aligns with traditional thermometers in heavy-ion collision analysis.

Abstract

By relating the charge multiplicity distribution and the temperature of a de-exciting nucleus through a deep neural network, we propose that the charge multiplicity distribution can be used as a thermometer of heavy-ion collisions. Based on an isospin-dependent quantum molecular dynamics model, we study the caloric curve of reaction $^{103}$Pd + $^9$Be with the apparent temperature determined through the charge multiplicity distribution. The caloric curve shows a characteristic signature of nuclear liquid-gas phase transition around the apparent temperature $T_{\rm ap}$ $=$ $6.4~\rm MeV$, which is consistent with that through a traditional heavy-ion collision thermometer, and indicates the viability of determining the temperature in heavy-ion collisions with multiplicity distribution.