Tau neutrinos at DUNE: new strategies, new opportunities

TL;DR

This paper introduces innovative analysis strategies for tau neutrino detection at DUNE, combining collider physics techniques and machine learning to improve signal discrimination and achieve significant sensitivity enhancements.

Contribution

It presents a novel analysis approach integrating collider physics methods and machine learning into neutrino measurements at DUNE, including the use of Rivet software.

Findings

Achieves $S/\sqrt{B}$ of 3.3 and 2.3 in nominal mode for hadronic and leptonic channels.

In tau-optimized mode, $S/\sqrt{B}$ increases to 8.8 and 11 for the respective channels.

Introduces the use of Rivet software in neutrino physics analysis.

Abstract

We propose a novel analysis strategy, that leverages the unique capabilities of the DUNE experiment, to study tau neutrinos. We integrate collider physics ideas, such as jet clustering algorithms in combination with machine learning techniques, into neutrino measurements. Through the construction of a set of observables and kinematic cuts, we obtain a superior discrimination of the signal ($S$) over the background ($B$). In a single year, using the nominal neutrino beam mode, DUNE may achieve $S/\sqrt{B}$ of $3.3$ and $2.3$ for the hadronic and leptonic decay channels of the tau respectively. Operating in the tau-optimized beam mode would increase $S/\sqrt{B}$ to $8.8$ and $11$ for each of these channels. We premier the use of the analysis software Rivet, a tool ubiquitously used by the LHC experiments, in neutrino physics. For wider accessibility, we provide our analysis code.