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.
Highlights
A key challenge to the success of experiments such as IceCube is the reliable and accurate reconstruction of events
In IceCube, further challenges arise as the detector is situated at the geographic South Pole where resources are limited
To perform real-time analyses and to issue alerts to telescopes around the world, powerful reconstruction methods are desired. This results in a dilemma as performance is often paired with computational complexity
Summary
A key challenge to the success of experiments such as IceCube is the reliable and accurate reconstruction of events. To perform real-time analyses and to issue alerts to telescopes around the world, powerful reconstruction methods are desired. This results in a dilemma as performance is often paired with computational complexity. Deep neural networks can be extremely powerful and their usage is computationally inexpensive once the networks are trained. These characteristics make an approach based on deep learning an excellent candidate for application in IceCube. Novel deep learning based methods tailored to the needs in high-energy physics experiments such as IceCube are needed
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