用神经网络提升伽马射线喷流方向精度，误差低于0.25度。
Evaluation of EAS directions based on TAIGA HiSCORE data using fully connected neural networks
- 用带跳跃连接的全连接网络，输入多个探测站数据估计方向。
- 两阶段算法使最终方向误差小于0.25度，性能优越。
- 适用于TAIGA多类型探测器融合分析，适合高能天体物理研究者。
广延空气簇射的方向可用于确定伽马量子源，并在估算初生粒子能量中起关键作用。TAIGA实验中的非成像切伦科夫探测阵列HiSCORE记录光电子数和探测时间，可高精度估计簇射方向。本文利用蒙特卡洛模拟的TAIGA HiSCORE伽马数据训练人工神经网络，采用多层感知机结合跳跃连接，以多个HiSCORE站的部分数据为输入，通过多个独立估计合成复合结果。采用两阶段算法，第一阶段的估计结果用于变换输入数据并优化后续估计。最终估计的平均误差低于0.25度。该方法将用于TAIGA实验中多种探测器的数据多模态分析。
原文摘要 · Abstract (English)
The direction of extensive air showers can be used to determine the source of gamma quanta and plays an important role in estimating the energy of the primary particle. The data from an array of non-imaging Cherenkov detector stations HiSCORE in the TAIGA experiment registering the number of photoelectrons and detection time can be used to estimate the shower direction with high accuracy. In this work, we use artificial neural networks trained on Monte Carlo-simulated TAIGA HiSCORE data for gamma quanta to obtain shower direction estimates. The neural networks are multilayer perceptrons with skip connections using partial data from several HiSCORE stations as inputs; composite estimates are derived from multiple individual estimates by the neural networks. We apply a two-stage algorithm in which the direction estimates obtained in the first stage are used to transform the input data and refine the estimates. The mean error of the final estimates is less than 0.25 degrees. The approach will be used for multimodal analysis of the data from several types of detectors used in the TAIGA experiment.
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