0000000000403100
AUTHOR
A. V. Balagopal
A search for time-dependent astrophysical neutrino emission with IceCube data from 2012 to 2017
Abstract High-energy neutrinos are unique messengers of the high-energy universe, tracing the processes of cosmic ray acceleration. This paper presents analyses focusing on time-dependent neutrino point-source searches. A scan of the whole sky, making no prior assumption about source candidates, is performed, looking for a space and time clustering of high-energy neutrinos in data collected by the IceCube Neutrino Observatory between 2012 and 2017. No statistically significant evidence for a time-dependent neutrino signal is found with this search during this period, as all results are consistent with the background expectation. Within this study period, the blazar 3C 279, showed strong var…
A Convolutional Neural Network based Cascade Reconstruction for the IceCube Neutrino Observatory
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 …