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.
翻译:在本篇文章中,我们使用人工智能算法来显示如何提高基本粒子轨道的分辨率,使之适应不相容的密度探测器,例如塑料焚化炉。我们利用深层次的学习来取代较传统的贝叶斯过滤法,大大改进了交互式粒子运动学的重建。我们表明,从自然语言处理领域继承的某种特定形式的神经网络非常接近采用超常信息规范的贝叶斯过滤器的概念。这种范式的改变可以影响未来的粒子物理学实验的设计及其数据利用。