A neural network approach for orienting heavy-ion collision events
Zu-Xing Yang (School of Physical Science and Technology, Southwest University, Chongqing, China, RIKEN Nishina Center, Wako, Japan)
; Xiao-Hua Fan (School of Physical Science and Technology, Southwest University, Chongqing, China, RIKEN Nishina Center, Wako, Japan); Zhi-Pan Li (School of Physical Science and Technology, Southwest University, Chongqing, China); Shunji Nishimura (RIKEN Nishina Center, Wako, Japan)
A convolutional neural network-based classifier is elaborated to retrace the initial orientation of deformed nucleus-nucleus collisions by integrating multiple typical experimental observables. The isospin-dependent Boltzmann-Uehling-Uhlenbeck transport model is employed to generate data for random orientations of ultra-central uranium-uranium collisions at . Statistically, the data-driven polarization scheme is essentially accomplished via the classifier, whose distinct categories filter out specific orientation-biased collision events. This will advance the deformed nucleus-based studies on nuclear symmetry energy, neutron skin, etc.
