0000000000236282
AUTHOR
Barbara Skrzypek
showing 3 related works from this author
Follow-up of Astrophysical Transients in Real Time with the IceCube Neutrino Observatory
2020
In multi-messenger astronomy, rapid investigation of interesting transients is imperative. As an observatory with a 4$\pi$ steradian field of view and $\sim$99\% uptime, the IceCube Neutrino Observatory is a unique facility to follow up transients, and to provide valuable insight for other observatories and inform their observing decisions. Since 2016, IceCube has been using low-latency data to rapidly respond to interesting astrophysical events reported by the multi-messenger observational community. Here, we describe the pipeline used to perform these follow up analyses and provide a summary of the 58 analyses performed as of July 2020. We find no significant signal in the first 58 analys…
LeptonInjector and LeptonWeighter: A neutrino event generator and weighter for neutrino observatories
2021
We present a high-energy neutrino event generator, called LeptonInjector, alongside an event weighter, called LeptonWeighter. Both are designed for large-volume Cherenkov neutrino telescopes such as IceCube. The neutrino event generator allows for quick and flexible simulation of neutrino events within and around the detector volume, and implements the leading Standard Model neutrino interaction processes relevant for neutrino observatories: neutrino-nucleon deep-inelastic scattering and neutrino-electron annihilation. In this paper, we discuss the event generation algorithm, the weighting algorithm, and the main functions of the publicly available code, with examples.
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 …