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.

翻译：广延大气簇射的方向可用于确定伽马量子的来源，并在估算原初粒子能量方面发挥重要作用。TAIGA实验中非成像切伦科夫探测器阵列HiSCORE记录的光电子数量与探测时间数据，可用于高精度估算簇射方向。本研究采用在蒙特卡罗模拟的TAIGA HiSCORE伽马量子数据上训练的人工神经网络来获取簇射方向估计值。该神经网络为包含跳跃连接的多层感知器，以多个HiSCORE站点的部分数据作为输入；通过神经网络将多个独立估计值综合为复合估计值。我们采用两阶段算法：第一阶段获得的方向估计值用于转换输入数据并优化估计结果。最终估计值的平均误差小于0.25度。该方法将用于TAIGA实验中多种类型探测器的多模态数据分析。