Combining Maximum-Likelihood with Deep Learning for Event Reconstruction in IceCube

Mirco Huennefeld, Rasha Abbasi, Markus Ackermann, Jenni Adams, Juanan Aguilar, M. Ahlers, Maryon Ahrens, Cyril Martin Alispach, Antonio Augusto Alves Junior, Najia Moureen Binte Amin, Rui An, Karen Andeen (+371 others)
2022 37th International Cosmic Ray Conference  
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
more » ... l 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.
doi:10.18154/rwth-2022-06025 fatcat:avu24ddh5jdtbmn3dra57twxju