A Neural Network Approach for Orienting Heavy-Ion Collision Events

TL;DR

This paper introduces a neural network classifier that accurately determines the initial orientation of deformed heavy-ion collisions, aiding studies on nuclear properties by filtering orientation-biased events.

Contribution

It presents a novel CNN-based method to identify collision orientations using simulated data, improving analysis of deformed nucleus collisions.

Findings

High accuracy in classifying collision orientations

Effective filtering of orientation-biased events

Potential to enhance nuclear structure studies

Abstract

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 $E_\text{beam} = 1\, \text{GeV/nucleon}$. 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.