论文标题
基于大气Cherenkov望远镜数据的基于卷积神经网络的系统不确定性的调查
Investigations of the Systematic Uncertainties in Convolutional Neural Network Based Analysis of Atmospheric Cherenkov Telescope Data
论文作者
论文摘要
通过使用卷积和经常性神经网络，机器学习是改善成像大气Cherenkov望远镜中背景拒绝性能的有前途的途径。但是，对于科学分析而言，至关重要的是，它们的性能在广泛的观察条件和仪器状态下保持稳定。 我们通过将卷积复发网络应用于Cherenkov望远镜阵列的玩具蒙特卡洛模拟中，研究了卷积复发网络的稳定性。然后，我们在模拟中改变了一系列观察和仪器参数。通常，大多数由此产生的系统学的水平不比传统分析要大得多。但是，发现神经网络预测对相机内噪声水平的强烈依赖性，在非常嘈杂的环境中，伽马射线的接受率高达50％。从这些研究中看到的性能差异可以清楚地看出，在最终分析的训练步骤中，在使用此类网络在Cherenkov望远镜观测中进行背景排斥时，必须考虑这些观察力效应。
Machine learning, through the use of convolutional and recurrent neural networks is a promising avenue for the improvement of background rejection performance in imaging atmospheric Cherenkov telescopes. However, it is of paramount importance for science analysis that their performance remains stable against a wide range of observing conditions and instrument states. We investigate the stability of convolutional recurrent networks by applying them to background rejection in a toy Monte Carlo simulation of a Cherenkov telescope array. We then vary a range of observation and instrument parameters in the simulation. In general, most of the resulting systematics are at a level not much greater than conventional analyses. However, a strong dependence of the neural network predictions on the noise level within the camera was found, with differences of up to 50% in the gamma-ray acceptance rate in very noisy environments. It is clear from the performance differences seen in these studies that these observational effects must be considered in the training step of the final analysis when using such networks for background rejection in Cherenkov telescope observations.
