Abstract

Fixed target experiments have an unique possibility to measure centrality of colliding systems by hadronic calorimeters on the beam line. This is usually achieved by the detection of all forward nucleon spectators and accomplishes fluctuation and correlation measures with lower biases than in collider experiments. However, hadronic calorimeters have much lower resolution than multiplicity detectors that introduces additional volume fluctuation to the measures. In this work, we present the first attempt to increase the resolution capacity of the spectator detector by implementing a convolutional neural network to the modular structure of the Projectile Spectator Calorimeter of the NA61/SHINE experiment. The data were generated in the framework of SHIELD Monte-Carlo event generator with detector response simulated with GEANT4 for the collisions of the lightest available system (⁷Be + ⁹Be) and for the highest beam momentum (150A GeV/c). Two ways of determination centrality—by a number of forward spectators and by forward energy – are considered. In comparison with the classical centrality selection method, the neural net shows a significant increase of centrality selection accuracy after implementation.

中文翻译：

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确定目标实验中卷积神经网络的集中度

摘要

固定目标实验具有一种独特的可能性，即可以通过强子热量计在光束线上测量碰撞系统的中心性。这通常是通过检测所有正向核子旁观者来实现的，并以比对撞机实验更低的偏差实现波动和相关性测量。但是，强子量热仪的分辨率要比多重检测器低得多，多重检测器给测量带来了额外的体积波动。在这项工作中，我们提出了通过对NA61 / SHINE实验的弹丸式观众热量计的模块化结构实施卷积神经网络来提高观众检测器的分辨能力的首次尝试。⁷ Be + ⁹ Be）和最高的光束动量（150 A GeV / c）。考虑了两种确定中心性的方法-由许多前向观众和前向能量-确定。与经典的中心性选择方法相比，神经网络在实施后显示出中心性选择精度的显着提高。