摘要:
人工神经网络方法已被引入高能物理实验领域并被广泛地应用于夸克胶子喷注的鉴别、电子强子分辨、顶夸克和Higgs 粒子的寻找等等。本文采用了一种改良的共轭梯度优化算法并应用于高能物理实验中粒子的鉴别。在该应用中，此算法既能实现每步迭代时在搜索方向上获得最优步长，又能避免目标函数陷入局部收敛点，从而使目标函数快速收敛，提高了算法的有效性。分析结果表明，我们改进后的BP算法显著地提高了粒子物理数据分析中的粒子鉴别能力。Artificial neural network methods have been introduced in high energy physics experiments and have been widely applied to the identification of the quark-gluon injection, electronic hadron discrimination,top quark, and the Higgs particle searching and so on. This paper introduces a modified conjugate gradient optimization algorithm, which is applied to the identification of high-energy particles. In the application, the algorithm can obtain optimal step size in the search direction for minimizing the objective function, and can overcome the local vibration problem, so that the fast convergence of the objective function is obtained and the stability of the algorithm is improved. The analysis of experimental data shows that our new BP neural network algorithm can effectively improve the identification of particles in high energy physics.
Abstract:
Artificial neural network methods have been introduced in high energy physics experiments and have been widely applied to the identification of the quark-gluon injection, electronic hadron discrimination,top quark, and the Higgs particle searching and so on. This paper introduces a modified conjugate gradient optimization algorithm, which is applied to the identification of high-energy particles. In the application, the algorithm can obtain optimal step size in the search direction for minimizing the objective function, and can overcome the local vibration problem, so that the fast convergence of the objective function is obtained and the stability of the algorithm is improved. The analysis of experimental data shows that our new BP neural network algorithm can effectively improve the identification of particles in high energy physics.
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