Blazars are a type of Active Galaxy that emit strong astrophysical jets. The association of a HE gamma-ray flare from the blazar TXS 0506+056 to the IceCube-170922A neutrino event in 2017, opened the possibility to a link between these two events. In this thesis, we will look at the HE gamma-ray behavior of TXS 0506+056 using data obtained from the Fermi-LAT by taking into account the other set of neutrino events associated with this source from 2014-2015. We will investigate whether both neutrino events present with comparable HE gamma-ray behavior by analyzing the lightcurves and the spectra for a quiet state, the 2014-2015 period, and the flare centered around the neutrino event from 2017. The results of the analysis performed in this thesis show no strong indication of a change in the gamma-ray behaviour in these potential neutrino detections.

Im Jahr 2013 ist es dem IceCube-Experiment gelungen, einen Fluss von Neutrinos extraterrestrischen Ursprungs nachzuweisen, und damit das Neutrino als weiteres kosmisches Botenteilchen zu etablieren. Die Frage nach dem Ursprung der Neutrinos, die einen komplementären Blick auf die Quellen bieten, kann möglicherweise die alte Frage nach dem Ursprung der kosmischen Strahlung lösen. Zeitunabhängige Suchen nach Neutrinoquellen konnten bisher keine einzelnen Kandidaten isolieren. Zeitlich veränderliche Quellen kommen daher als Ursprung in Betracht. IceCube ermöglicht es, kontinuierlich den gesamten Himmel nach aufflackernden Neutrinoquellen abzusuchen und die astronomische Gemeinschaft schnellstmöglich zu benachrichtigen. In dieser Arbeit wird die Echtzeitidentifikation und -rekonstruktion von Myonneutrinokandidaten mit IceCube verbessert. Die erreichte Sensitivität ist mit etablierten nicht-Echtzeit Analysen vergleichbar. Kontinuierlich vom Experiment am Südpol übermittelte Informationen werden sofort auf bemerkenswerte Ereignisse hin analysiert. Bekannte astrophysikalische Quellen von Gammastrahlung werden auf Neutrinoemission hin beobachtet. Eine verallgemeinerte Methode erlaubt die Suche nach Signalen überall, unbeeinflusst von vorher bekannten Quellen. Weiterhin werden die hochenergetischsten Neutrinokandidaten, die wahrscheinlich astrophysikalischen Ursprungs sind, sofort identifiziert und global bekannt gemacht. Abschließend werden die Suchalgorithmen am Beispiel zweier Blazare demonstiert, 1ES 1959+650 und TXS 0506+56. In letzterem Fall wurden erstmals Anzeichen für eine Quelle hochenergetischer, kosmischer Neutrinos gefunden. Die im Rahmen dieser Arbeit entwickelte Infrastruktur erlaubt es, die astronomische Gemeinschaft auf signifikante Neutrinoereignisse, oder sich entwickelnde Neutrinocluster hinzuweisen. Auch die zügige Suche nach Neutrinos in Reaktion auf interessante astrophysikalische Ereignisse, wie z.B. Gravitationswellen, ist möglich. / The discovery of an flux of neutrinos of astrophysical origin with the IceCube experiment in 2013 has broadened our understanding of cosmic messengers and opened a new window on the universe. By addressing the newly pertinent question about their sources, neutrinos can provide a complementary view on cosmic accelerators and may help solving the long-standing puzzle of the origin of the cosmic rays. As traditional time-integrated searches for sources of neutrinos have not been able to isolate individual candidates, variable and transient sources shift into focus. IceCube's design allows to continuously search the entire sky for neutrino flares, and alert the community with the lowest possible latency in the case of a detection. This thesis improves the identification and reconstruction of muon neutrino candidates with IceCube in real-time, achieving a sensitivity comparable to dedicated offline analyses. The stream of neutrino candidates is analyzed for interesting events in order to alert partner experiments and inspire follow-up observations. First, known gamma-ray emitters are monitored for time-variable neutrino emission. Second, a generalization of this method monitors the entire sky for neutrino flares, regardless of pre-defined source lists. Third, the most-energetic neutrino candidates with the highest chance to be of astrophysical origin are selected for alerts. Eventually, the search methods are applied to the blazars 1ES 1959+650 and TXS 0506+056. In the latter case, evidence for source of high-energetic, astrophysical neutrinos was found for the first time. The infrastructure built in this work allows to notify the community whenever significant neutrino events are recorded, or significant flares develop on time-scales from days to weeks. It also allows to quickly perform neutrino follow-up searches in response to interesting astrophysical events, such as the observation of gravitational waves.

Neutrino astronomy is expanding into the ultra-high energy (>1017eV) frontier with the use of in-ice detection of Askaryan radio emission from neutrino-induced particle showers. There are already pilot arrays for validating the technology and the next few years will see the planning and construction of IceCube-Gen2, an upgrade to the current neutrino telescope IceCube. This thesis aims to facilitate that planning by providing faster simulations using deep learning surrogate models. Faster simulations could enable proper optimisation of the antenna stations providing better sensitivity and reconstruction of neutrino properties. The surrogates are made for two parts of the end-to-end simulations: the signal generation and the signal propagation. These two steps are the most time-consuming parts of the simulations. The signal propagation is modelled with a standard fully connected neural network whereas for the signal generation a conditional Wasserstein generative adversarial network is used. There are multiple reasons for using these types of models. For both problems the neural networks provide the speed necessary as well as being differentiable -both important factors for optimisation. Generative adversarial networks are used in the signal generation because of the inherent stochasticity in the particle shower development that leads to the Askaryan radio signal. A more standard neural network is used for the signal propagation as it is a regression task. Promising results are obtained for both tasks. The signal propagation surrogate model can predict the parameters of interest at the desired accuracy, except for the travel time which needs further optimisation to reduce the uncertainty from 0.5 ns to 0.1 ns. The signal generation surrogate model predicts the Askaryan emission well for the limited parameter space of hadronic showers and within 5° of the Cherenkov cone. The two models provide a first step and a proof of concept. It is believed that the models can reach the required accuracies with more work.

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