New deep learning techniques present promising new analysis methods for Imaging Atmospheric Cherenkov Telescopes (IACTs) such as the upcoming Cherenkov Telescope Array (CTA). In particular, the use of Convolutional Neural Networks (CNNs) could provide a direct event classification method that uses the entire information contained within the Cherenkov shower image, bypassing the need to Hillas parameterise the image and allowing fast processing of the data.

Existing work in this field has utilised images of the integrated charge from IACT camera photomultipliers, however the majority of current and upcoming generation IACT cameras have the capacity to read out the entire photosensor waveform following a trigger. As the arrival times of Cherenkov photons from Extensive Air Showers (EAS) at the camera plane are dependent upon the altitude of their emission and the impact distance from the telescope, these waveforms contain information potentially useful for IACT event classification.

In this test-of-concept simulation study, we investigate the potential for using these camera pixel waveforms with new deep learning techniques as a background rejection method, against both proton and electron induced EAS. We find that a means of utilising their information is to create a set of seven additional 2-dimensional pixel maps of waveform parameters, to be fed into the machine learning algorithm along with the integrated charge image. Whilst we ultimately find that the only classification power against electrons is based upon event direction, methods based upon timing information appear to out-perform similar charge based methods for gamma/hadron separation. We also review existing methods of event classifications using a combination of deep learning and timing information in other astroparticle physics experiments.

新的深度学习技术为诸如即将来临的Cherenkov望远镜阵列（CTA）等大气Cherenkov望远镜（IACT）提供了有希望的新分析方法。特别是，卷积神经网络（CNN）的使用可以提供一种直接事件分类方法，该方法将使用Cherenkov阵雨图像中包含的全部信息，而无需通过Hillas对图像进行参数化并允许对数据进行快速处理。

该领域的现有工作利用了来自IACT相机光电倍增管的积分电荷的图像，但是，当前和下一代的大多数IACT相机都具有在触发后读出整个光电传感器波形的能力。由于来自大量空气淋浴（EAS）的契伦科夫光子到达相机平面的时间取决于其发射的高度和距望远镜的撞击距离，因此这些波形包含的信息可能对IACT事件分类有用。

在此概念测试的模拟研究中，我们研究了将这些相机像素波形与新的深度学习技术作为背景抑制方法一起使用的潜力，以对抗质子和电子诱导的EAS。我们发现，利用它们的信息的一种方法是创建一组七个额外的波形参数二维像素图，与集成电荷图像一起馈入机器学习算法。尽管我们最终发现针对电子的唯一分类能力是基于事件方向的，但基于定时信息的方法似乎胜过了类似的基于电荷的伽马/强子分离方法。我们还将在其他天体物理实验中结合使用深度学习和定时信息来回顾现有的事件分类方法。