Reconstruction Techniques in IceCube using Convolutional and Generative Neural Networks

Abstract

Reliable and accurate reconstruction methods are vital to the success of high-energy physics experiments such as IceCube. Machine learning based techniques, in particular deep neural networks, can provide a viable alternative to maximum-likelihood methods. However, most common neural network architectures were developed for other domains such as image recogntion. While these methods can enhance the reconstruction performance in IceCube, there is much potential for tailored techniques. In the typical physics use-case, many symmetries, invariances and prior knowledge exist in the data, which are not fully exploited by current network architectures. Novel and specialized deep learning based reconstruction techniques are desired which can leverage the physics potential of experiments like IceCube. A reconstruction method using convolutional neural networks is presented which can significantly increase the reconstruction accuracy while greatly reducing the runtime in comparison to standard reconstruction methods in Ice- Cube. In addition, first results are discussed for future developments based on generative neural networks.