论文标题
人工智能改善了浸入磁场中的非均匀致密材料中基本颗粒轨迹的拟合
Artificial intelligence for improved fitting of trajectories of elementary particles in inhomogeneous dense materials immersed in a magnetic field
论文作者
论文摘要
在本文中，我们使用人工智能算法来展示如何增强基本粒子轨迹拟合在不均匀浓密探测器（例如塑料闪烁体）中的分辨率。我们使用深度学习来代替更多传统的贝叶斯过滤方法，从而大大改善了相互作用的粒子运动学的重建。我们表明，从自然语言处理领域继承的一种特定形式的神经网络非常接近贝叶斯过滤器的概念，该贝叶斯过滤器采用了过度信息。这样的范式变化会影响未来粒子物理实验及其数据开发的设计。
In this article, we use artificial intelligence algorithms to show how to enhance the resolution of the elementary particle track fitting in inhomogeneous dense detectors, such as plastic scintillators. We use deep learning to replace more traditional Bayesian filtering methods, drastically improving the reconstruction of the interacting particle kinematics. We show that a specific form of neural network, inherited from the field of natural language processing, is very close to the concept of a Bayesian filter that adopts a hyper-informative prior. Such a paradigm change can influence the design of future particle physics experiments and their data exploitation.
