Characterizing the Astrophysical Neutrino Flux Using Contained and Uncontained Cascade Events

TL;DR

This paper uses advanced neural network techniques to analyze IceCube cascade events, improving detection efficiency and clarifying the astrophysical neutrino spectrum from 1 TeV to 100 PeV, revealing deviations from a simple power law.

Contribution

It introduces a neural network-based method for selecting contained and uncontained cascade events, significantly enhancing effective area and spectral analysis capabilities.

Findings

Improved effective area by a factor of ~3 over previous methods.

Detected deviations from a single power law in the neutrino flux.

Enhanced modeling of atmospheric neutrino background.

Abstract

Recently, the IceCube Neutrino Observatory has reported a deviation from the single power law in the extragalactic diffuse neutrino flux. A neural network-based event selection of contained and uncontained cascade events from IceCube, in which uncontained events have interaction vertices at the edge or outside of the detector instrumentation volume, has a factor ~3 gain in effective area over the cascade events used in the novel combined tracks and cascades selection which reported the deviation. Systematic improvements and rigorously updated modeling of the atmospheric neutrino background is incorporated into this high statistics contained and uncontained cascade event selection to clarify features of the astrophysical neutrino spectrum across energies from 1 TeV up to 100 PeV.