Reconstruction of Neutrino Events in IceCube using Graph Neural Networks. (arXiv:2107.12187v1 [astro-ph.IM])
<a href="http://arxiv.org/find/astro-ph/1/au:+Minh_M/0/1/0/all/0/1">Martin Ha Minh</a> (for the IceCube Collaboration)
The IceCube Neutrino Observatory is a cubic-kilometer scale neutrino detector
embedded in the Antarctic ice of the South Pole. In the near future, the
detector will be augmented by extensions, such as the IceCube Upgrade and the
planned Gen2 detector. The sparseness of observed light in the detector for
low-energy events, and the irregular detector geometry, have always been a
challenge to the reconstruction of the detected neutrinos’ parameters of
interest. This challenge remains with the IceCube Upgrade, currently under
construction, which introduces seven new detector strings with novel detector
modules. The Upgrade modules will increase the detection rate of low-energy
events and allow us to further constrain neutrino oscillation physics. However,
the geometry of these modules render existing traditional reconstruction
algorithms more difficult to use. We introduce a new reconstruction algorithm
based on Graph Neural Networks, which we use to reconstruct neutrino events at
much faster processing times than the traditional algorithms, while providing
comparable resolution. We show that our algorithm is applicable not only to
reconstructing data of the current IceCube detector, but also simulated events
for next-generation extensions, such as the IceCube Upgrade.
The IceCube Neutrino Observatory is a cubic-kilometer scale neutrino detector
embedded in the Antarctic ice of the South Pole. In the near future, the
detector will be augmented by extensions, such as the IceCube Upgrade and the
planned Gen2 detector. The sparseness of observed light in the detector for
low-energy events, and the irregular detector geometry, have always been a
challenge to the reconstruction of the detected neutrinos’ parameters of
interest. This challenge remains with the IceCube Upgrade, currently under
construction, which introduces seven new detector strings with novel detector
modules. The Upgrade modules will increase the detection rate of low-energy
events and allow us to further constrain neutrino oscillation physics. However,
the geometry of these modules render existing traditional reconstruction
algorithms more difficult to use. We introduce a new reconstruction algorithm
based on Graph Neural Networks, which we use to reconstruct neutrino events at
much faster processing times than the traditional algorithms, while providing
comparable resolution. We show that our algorithm is applicable not only to
reconstructing data of the current IceCube detector, but also simulated events
for next-generation extensions, such as the IceCube Upgrade.
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