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Measurement of atmospheric neutrino oscillation parameters using convolutional neural networks with 9.3 years of data in IceCube DeepCore
IceCube Collaboration
The DeepCore sub-detector of the IceCube Neutrino Observatory provides access to neutrinos with energies above approximately 5 GeV. Data taken between 2012-2021 (3,387 days) are ut…
Neutron Tagging following Atmospheric Neutrino Events in a Water Cherenkov Detector
K. Abe, Y. Haga, Y. Hayato +301
We present the development of neutron-tagging techniques in Super-Kamiokande IV using a neural network analysis. The detection efficiency of neutron capture on hydrogen is estimate…
A muon-track reconstruction exploiting stochastic losses for large-scale Cherenkov detectors
R. Abbasi, M. Ackermann, J. Adams +362
IceCube is a cubic-kilometer Cherenkov telescope operating at the South Pole. The main goal of IceCube is the detection of astrophysical neutrinos and the identification of their s…
A Convolutional Neural Network based Cascade Reconstruction for the IceCube Neutrino Observatory
R. Abbasi, M. Ackermann, J. Adams +364
Continued improvements on existing reconstruction methods are vital to the success of high-energy physics experiments, such as the IceCube Neutrino Observatory. In IceCube, further…
Measurement of the high-energy all-flavor neutrino-nucleon cross section with IceCube
R. Abbasi, M. Ackermann, J. Adams +361
The flux of high-energy neutrinos passing through the Earth is attenuated due to their interactions with matter. The interaction rate is modulated by the neutrino interaction cross…
Searching for eV-scale sterile neutrinos with eight years of atmospheric neutrinos at the IceCube neutrino telescope
M. G. Aartsen, R. Abbasi, M. Ackermann +373
We report in detail on searches for eV-scale sterile neutrinos, in the context of a 3+1 model, using eight years of data from the IceCube neutrino telescope. By analyzing the recon…