论文标题
探索从神经网络模型中恢复物理过程属性的可能性
Exploring the Possibility of a Recovery of Physics Process Properties from a Neural Network Model
论文作者
论文摘要
机器学习方法在粒子物理学上的应用通常没有足够的理解对基础物理学。一个可解释的模型，它提供了一种方法，可以直接从数据中提高我们对管理物理系统的机制的了解，这是非常有用的。在本文中，我们基于量子染色体动力学（QCD）破碎过程引入了一个简单的人造物理发生器。然后，从发电机模拟的数据将传递给神经网络模型，我们仅基于发电机的部分知识。我们的目的是查看生成数据的解释是否可以提供此类物理系统基本过程的概率分布。这样，我们目的省略了我们从网络模型中省略的一些信息。我们认为，这种方法在分析实际QCD过程中可能是有益的。
The application of machine learning methods to particle physics often doesn't provide enough understanding of the underlying physics. An interpretable model which provides a way to improve our knowledge of the mechanism governing a physical system directly from the data can be very useful. In this paper, we introduce a simple artificial physical generator based on the Quantum chromodynamical (QCD) fragmentation process. The data simulated from the generator are then passed to a neural network model which we base only on the partial knowledge of the generator. We aim to see if the interpretation of the generated data can provide the probability distributions of basic processes of such a physical system. This way, some of the information we omitted from the network model on purpose is recovered. We believe this approach can be beneficial in the analysis of real QCD processes.
