Status of a Deep Learning Based Measurement of the Inclusive Muon Neutrino Charged-current Cross Section in the NOvA Near Detector

TL;DR

This paper reports on a deep learning approach to measure the inclusive muon neutrino charged-current cross section using the NOvA Near Detector, leveraging convolutional neural networks for event identification.

Contribution

It introduces new deep learning algorithms for neutrino event classification and presents the status of a novel cross section measurement in the NOvA experiment.

Findings

Implementation of convolutional neural networks for event identification.

Initial results demonstrating the effectiveness of deep learning in neutrino cross section measurement.

Enhanced accuracy in neutrino interaction analysis.

Abstract

NOvA is a long-baseline neutrino oscillation experiment. It uses the NuMI beam from Fermilab and two sampling calorimeter detectors placed off-axis from the beam. The 293 ton Near Detector measures the unoscillated neutrino energy spectrum, which can be used to predict the neutrino energy spectrum observed at the 14 kton Far Detector. The Near Detector also provides an excellent opportunity to measure neutrino interaction cross sections with high statistics, which will benefit current and future long-baseline neutrino oscillation experiments. This analysis implements new algorithms to identify $\nu_{\mu}$ charge-current events by using visual deep learning tools such as convolutional neural networks. We present the status of a measurement of the inclusive $\nu_{\mu}$ CC cross section in the NOvA Near Detector.