Continued improvements on existing reconstruction methods are vital to the success of high-energy physics experiments, such as the IceCube Neutrino Observatory. In IceCube, further challenges arise as the detector is situated at the geographic South Pole where computational resources are limited. However, to perform real-time analyses and to issue alerts to telescopes around the world, powerful and fast reconstruction methods are desired. Deep neural networks can be extremely powerful, and their usage is computationally inexpensive once the networks are trained. These characteristics make a deep learning-based approach an excellent candidate for the application in IceCube. A reconstruction method based on convolutional architectures and hexagonally shaped kernels is presented. The presented method is robust towards systematic uncertainties in the simulation and has been tested on experimental data. In comparison to standard reconstruction methods in IceCube, it can improve upon the reconstruction accuracy, while reducing the time necessary to run the reconstruction by two to three orders of magnitude.
翻译:现有重建方法的不断改进对于高能物理实验的成功至关重要,例如IceCube Neutrino观测站。在IceCube中,由于探测器位于计算资源有限的南极地理上,因此出现了进一步的挑战。然而,为了进行实时分析并向世界各地的望远镜发出警报,需要采用强大而迅速的重建方法。深神经网络可能非常强大,一旦网络培训,其使用在计算上就非常便宜。这些特点使得深学习方法成为在ICecube应用的极好候选方法。提出了基于革命建筑和六角形内核的重建方法。所提出的方法对模拟中的系统性不确定性十分有力,并且已经对实验数据进行了测试。与IceCube的标准重建方法相比,它可以提高重建的准确性,同时将重建所需的时间缩短2至3级。