光电传感器定时信息与机器学习技术在背景抑制中的应用前景
Prospects for the Use of Photosensor Timing Information with Machine Learning Techniques in Background Rejection
摘要：
机器学习（ML）技术的最新发展为天体粒子物理实验中的高速成像提供了一种有前途的新分析方法，例如成像大气切伦科夫望远镜（IACT）。特别是，使用新的机器学习技术的定时信息提供了一种新的方法，用于事件分类。该领域的先前工作利用了来自IACT相机光电倍增管的积分电荷的图像，但是大多数当前和即将到来的IACT相机具有在触发之后读出整个光电传感器波形的能力。由于切伦科夫光子从广泛的空气淋浴（EAS）在相机平面的到达时间取决于其发射的高度，这些波形包含IACT事件分类有用的信息。在这项工作中，我们研究了将这些波形与ML技术结合使用的可能性，并发现利用它们的信息的一种非常有效的方法是创建一组七个额外的波形参数二维直方图，这些波形参数将与集成电荷图像一起输入机器学习算法。这似乎优于仅使用这些新的ML技术与单独的波形积分电荷。我们还在其他实验的背景下研究了这些基于时间的ML技术。
英文摘要：
Recent developments in machine learning (ML) techniques present a promising new analysis method for high-speed imaging in astroparticle physics experiments, for example with imaging atmospheric Cherenkov telescopes (IACTs). In particular, the use of timing information with new machine learning techniques provides a novel method for event classification. Previous work in this field has utilised images of the integrated charge from IACT camera photomultipliers, but the majority of current and upcoming 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, these waveforms contain information useful for IACT event classification. In this work, we investigate the potential for using these waveforms with ML techniques, and find that a highly effective means of utilising their information is to create a set of seven additional two dimensional histograms of waveform parameters to be fed into the machine learning algorithm along with the integrated charge image. This appears to be superior to using only these new ML techniques with the waveform integrated charge alone. We also examine these timing-based ML techniques in the context of other experiments.
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