Improved measurements of tau neutrinos are crucial for testing the
three-neutrino hypothesis and improving our understanding of leptonic mixing.
The DUNE experiment offers an unprecedented opportunity to measure events
involving tau neutrinos. We propose an analysis strategy that borrows ideas
typically applied at collider experiments to separate signal from background
signatures. We simulate the former using GiBUU. A novel aspect of this work is
the usage of the analysis software Rivet, which is a tool ubiquitously used by
the LHC experiments. We apply techniques novel to neutrino physics, such as
jet algorithms in combination with machine learning techniques, to construct a
set of observables and kinematic cuts which allow for superior discrimination
of signal over the background.
Analysis to detect tau neutrinos at DUNE using novel techniques.