# Measurement of the Diffuse Muon Neutrino Flux using Starting Track Events in IceCube

## TL;DR

This paper reports on measuring the diffuse astrophysical muon neutrino flux in IceCube using starting track events and machine learning to distinguish between atmospheric and astrophysical neutrinos, enhancing understanding of neutrino spectra.

## Contribution

Introduces a new method using starting track events and machine learning in IceCube to improve measurement of the diffuse muon neutrino flux above 1 TeV.

## Key findings

- High purity astrophysical neutrino sample achieved
- Demonstrated sensitivity with simulated neutrino data
- Enhanced differentiation between atmospheric and astrophysical neutrinos

## Abstract

IceCube measures the diffuse neutrino flux across several neutrino flavors and energy ranges. ESTES focuses on the measurement of the diffuse neutrino flux using high purity astrophysical muon neutrinos with energies above 1 TeV. We use the Enhanced Starting Track Event Selection dataset which selects for tracks starting within the IceCube fiducial volume. We employ a machine learning algorithm to help differentiate between tracks from atmospheric and astrophysical neutrinos. This produces a high purity diffuse neutrino sample that can provide valuable insight into the properties of the atmospheric and astrophysical diffuse neutrino spectrum. Using simulated neutrinos, we show the sensitivity for this measurement of the diffuse neutrino flux with ESTES.