A Data-Driven Method of Background Prediction at NOvA

TL;DR

This paper presents a data-driven technique for estimating the neutral current background in the NOvA neutrino experiment by analyzing muon-removed charged current interactions to model NC-like events.

Contribution

It introduces a novel method to predict background spectra using muon-removed charged current events, improving background estimation accuracy in neutrino oscillation studies.

Findings

Effective modeling of NC background using muon-removed events

Enhanced background prediction accuracy for NOvA

Potential application to other neutrino experiments

Abstract

NOvA is a long-baseline neutrino oscillation experiment that will use the NuMI beam originating at Fermilab. NOvA enables the study of two oscillation channels: $\nu_{\mu}$ disappearance and $\nu_{e}$ appearance. It consists of two functionally identical detectors, the near detector (ND) at Fermilab and the far detector (FD) near International Falls in Northern Minnesota. The ND will be used to study the neutrino beam spectrum and composition before oscillation, and measure background rate to the $\nu_e$ appearance search. In this paper, I describe a data-driven technique to estimate the neutral current (NC) component of the ND spectrum. Using the $\nu_{\mu}$ CC interactions where the reconstructed muon is removed from the event, we produce a well understood sample of hadronic showers that resemble NC interactions.