Determination of impact parameter for CEE with Digi-input neural networks

Author:

Botan Wang, Yi Wang, Dong Han, Zhigang Xiao, Yapeng Zhang

Keyword:

High Energy Physics - Experiment, High Energy Physics - Experiment (hep-ex), Instrumentation and Detectors (physics.ins-det)

journal:

date:

2023-07-27 16:00:00

Abstract

Impact parameter is an important quantity which characterizes the centrality in nucleus-nucleus collision geometry. The determination of impact parameter in real experiments takes use of the hits on detector system or the reconstructed tracks of the secondary particles. As a task of feature recognition, methods such as sharp cut-off, Bayesian methods and Neural Networks (NN) has been studied and applied. However, in the situation of the Cooler-storage-ring External-target Experiment (CEE), the low beam energy brings a lapse of dependency between impact parameter and charged particle multiplicity, which decreases the validity of the explicit determination methods. This work proposes a regressor constructed with Graph Attention neural network, which takes the hit-level data as input. This model has shown a mean absolute error of 0.496 fm for the IQMD collision data of the UU system at 0.5 AMeV. The performance of such a model is compared with reference models, showing its capacity in handling the original but potentially interrelated digi information.

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