REVIEW 1 cited by
Determining the temperature in heavy-ion collisions with multiplicity distribution
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
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.
Forward citations
Cited by 1 Pith paper
-
Validation and extrapolation of atomic mass with physics-informed fully connected neural network
A physics-informed neural network predicts nuclear binding energies to about 0.1 MeV and reproduces pairing and shell effects, with extrapolation tested against new AME2020 data.
Discussion (0). Continue with ORCID to comment.