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Determining the temperature in heavy ion collisions with multiplicity distribution
Physics Letters B ( IF 4.3 ) Pub Date : 2021-03-01 , DOI: 10.1016/j.physletb.2021.136084
Yi-Dan Song , Rui Wang , Yu-Gang Ma , Xian-Gai Deng , Huan-Ling Liu

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:

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。
更新日期:2021-03-01
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