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T: Fachverband Teilchenphysik

T 23: Postersitzung

T 23.4: Poster

Montag, 29. Februar 2016, 13:30–14:30, VMP4 Foyer

Unbinned likelihood maximisation framework for neutrino clustering in Python — •Stefan Coenders — Technische Universität München, Boltzmannstr. 2, 85748 Garching

Albeit having detected an astrophysical neutrino flux with IceCube, sources of astrophysical neutrinos remain hidden up to now. A detection of a neutrino point source is a smoking gun for hadronic processes and acceleration of cosmic rays. The search for neutrino sources has many degrees of freedom, for example steady versus transient, point-like versus extended sources, et cetera. Here, we introduce a Python framework designed for unbinned likelihood maximisations as used in searches for neutrino point sources by IceCube. Implementing source scenarios in a modular way, likelihood searches on various kinds can be implemented in a user-friendly way, without sacrificing speed and memory management.