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T: Fachverband Teilchenphysik

T 42: Neutrino astronomy 2

T 42.2: Vortrag

Dienstag, 5. März 2024, 16:15–16:30, Geb. 30.23: 6/1

Binning Optimization of the Likelihood Analysis of Astrophysical Muon Neutrinos with IceCube using an Evolutionary Algorithm — •Matthias Thiesmeyer, Jakob Böttcher, Shuyang Deng, Philipp Fürst, Erik Ganster, Sharif El Mentawi, and Christopher Wiebusch for the IceCube collaboration — III. Physikalisches Insitut b, RWTH Aachen University

One important detection channel for astrophysical neutrinos in the IceCube Neutrino Observatory is neutrino-induced muon tracks. The astrophysical flux parameters are estimated using a profile likelihood fit of the measured neutrino data. The binned 2D distribution of reconstructed zenith and energy is compared to the number of expected events from atmospheric and astrophysical neutrino fluxes. To maximize the sensitivity to the astrophysical neutrino flux properties, we optimize the choice of binning. First, we extend the simple Poissonian likelihood to an effective likelihood that includes the uncertainties of the bin predictions caused by limited Monte-Carlo statistics. Then, using the effective likelihood, we apply an evolutionary algorithm for binning optimization. Iteratively, the algorithm creates different candidate binnings, compares them, and selects the best performing binning to create new candidates from. This talk highlights the different properties of both likelihoods for binning optimization, describes the evolutionary algorithm, and discusses the result.

Keywords: Neutrino; Astronomy; Astrophysics; IceCube