Combining Maximum-Likelihood with Deep Learning for Event Reconstruction in IceCube
This work addresses the problem of inefficient event reconstruction in particle physics experiments like IceCube by integrating domain knowledge into deep learning, though it is incremental as it builds on existing hybrid approaches.
The paper tackles the challenge of event reconstruction in particle physics by combining deep learning with maximum-likelihood methods, using generative neural networks to approximate likelihoods and incorporate domain knowledge, resulting in improved computational efficiency and accuracy for IceCube data.
The field of deep learning has become increasingly important for particle physics experiments, yielding a multitude of advances, predominantly in event classification and reconstruction tasks. Many of these applications have been adopted from other domains. However, data in the field of physics are unique in the context of machine learning, insofar as their generation process and the laws and symmetries they abide by are usually well understood. Most commonly used deep learning architectures fail at utilizing this available information. In contrast, more traditional likelihood-based methods are capable of exploiting domain knowledge, but they are often limited by computational complexity. In this contribution, a hybrid approach is presented that utilizes generative neural networks to approximate the likelihood, which may then be used in a traditional maximum-likelihood setting. Domain knowledge, such as invariances and detector characteristics, can easily be incorporated in this approach. The hybrid approach is illustrated by the example of event reconstruction in IceCube.