0000000000878411

AUTHOR

J. B. Tjus

showing 1 related works from this author

A Convolutional Neural Network based Cascade Reconstruction for the IceCube Neutrino Observatory

2021

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 challenges arise as the detector is situated at the geographic South Pole where computational resources are limited. However, to perform real-time analyses and to issue alerts to telescopes around the world, powerful and fast reconstruction methods are desired. Deep neural networks can be extremely powerful, and their usage is computationally inexpensive once the networks are trained. These characteristics make a deep learning-based approach an excellent candidate for the application in IceCube. A reconstruction …

FOS: Computer and information sciencesComputer Science - Machine LearningAstrophysics::High Energy Astrophysical Phenomenacs.LGData analysisFOS: Physical sciencesFitting methods01 natural sciencesConvolutional neural networkCalibration; Cluster finding; Data analysis; Fitting methods; Neutrino detectors; Pattern recognitionHigh Energy Physics - ExperimentIceCube Neutrino ObservatoryMachine Learning (cs.LG)High Energy Physics - Experiment (hep-ex)Pattern recognition0103 physical sciencesNeutrino detectors010303 astronomy & astrophysicsInstrumentationMathematical Physics010308 nuclear & particles physicsbusiness.industryhep-exDeep learningCluster findingDetectorNeutrino detectorComputer engineeringOrders of magnitude (time)13. Climate actionCascadeCalibrationPattern recognition (psychology)Artificial intelligencebusiness
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