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 Pd + 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 Tap = 6.4 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.

中文翻译：

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用多重分布确定重离子碰撞中的温度

通过深度神经网络将电荷多重性分布与去激核的温度相关联，我们提出电荷多重性分布可用作重离子碰撞的温度计。基于同位旋相关的量子分子动力学模型，我们研究了反应 Pd + Be 的热量曲线，其表观温度通过电荷多重分布确定。热量曲线显示了在表观温度 Tap = 6.4 MeV 附近核液气相变的特征特征，与传统重离子碰撞温度计的结果一致，表明重离子碰撞温度测定的可行性具有多重分布。