With many applications in high-energy physics, Deep Learning or Deep Neural Network (DNN) has become noticeable and practical in recent years. In this article, a new technique is presented for modeling the charged particles multiplicity distribution [Formula: see text] of Proton-Proton [Formula: see text] collisions using an efficient DNN model. The charged particles multiplicity n, the total center of mass energy [Formula: see text], and the pseudorapidity [Formula: see text] used as input in DNN model and the desired output is [Formula: see text]. DNN was trained to build a function, which studies the relationship between [Formula: see text]. The DNN model showed a high degree of consistency in matching the data distributions. The DNN model is used to predict with [Formula: see text] not included in the training set. The expected [Formula: see text] had effectively merged the experimental data and the values expected indicate a strong agreement with Large Hadron Collider (LHC) for ATLAS measurement at [Formula: see text], 7 and 8 TeV.

中文翻译：

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在 LHC 上模拟带电粒子多重性分布

近年来，随着在高能物理中的许多应用，深度学习或深度神经网络 (DNN) 变得引人注目和实用。在本文中，提出了一种新技术，用于使用有效的 DNN 模型对质子-质子 [公式：参见文本] 碰撞的带电粒子多重性分布 [公式：参见文本] 进行建模。在 DNN 模型中用作输入的带电粒子多重性 n、总质心能量 [公式：见文本] 和伪快速性 [公式：见文本]，所需输出为 [公式：见文本]。训练 DNN 构建一个函数，该函数研究 [公式：见正文] 之间的关系。DNN 模型在匹配数据分布方面表现出高度的一致性。DNN 模型用于预测 [公式：见文本] 未包含在训练集中。预期的[公式：