多奇异数重子鉴别中信号样本统计性问题的蒙特卡洛法研究

Identification of Multi-strange Baryons for Statistics of Singal Sample

为了解决多奇异数重子两步拓扑重建效率低的问题,利用人工神经网络以较高的效率来一步完成对奇异粒子的鉴别。就高维参数空间中BP神经网络对训练样本高统计量要求的问题,进行了蒙特卡洛法研究,所得结论支持用人工神经网络鉴别多奇异数重子。

In order to solve the problem of low efficiency in two-step topological reconstruction of the multi-strange baryons it is planed to apply the artificial neural network to the identification of the baryons in one step with higher efficiency. A Monte Carlo study is carried out on the problem of demanding high statistics of the training sample by BP neural network in a high dimensional parameter space. The results support the application of the neural network to the identification of multi-strange baryons.