Journal
PHYSICS LETTERS B
Volume 814, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.physletb.2021.136084
Keywords
Heavy-ion collisions; Temperature; Machine learning; Multiplicity
Funding
- National Natural Science Foundation of China [11890710, 11890714, 11625521]
- Key Research Program of Frontier Sciences of the CAS [QYZDJ-SSW-SLH002]
- Strategic Priority Research Program of the CAS [XDB34000000]
- Guangdong Major Project of Basic and Applied Basic Research [2020B0301030008]
- Postdoctoral Innovative Talent Program of China [BX20200098]
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By using the charge multiplicity distribution as a thermometer of heavy-ion collisions, researchers found characteristic signatures of nuclear liquid-gas phase transition around the apparent temperature, confirming the viability of determining the temperature in heavy-ion collisions with multiplicity distribution.
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-103 + Be-9 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-ap = 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. (C) 2021 The Authors. Published by Elsevier B.V.
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