Measurement of All Flavor PeV Neutrino Flux using Combined Datasets from IceCube

TL;DR

This paper presents a comprehensive measurement of all-flavor PeV neutrino flux using combined datasets from IceCube, employing neural network event selection and improved background modeling to clarify the astrophysical neutrino spectrum from 1 TeV to 100 PeV.

Contribution

It introduces a neural network-based event selection method with increased effective area and incorporates systematic background modeling to enhance neutrino flux measurements.

Findings

Detection of deviations from a single power law in the neutrino spectrum.

Enhanced effective area (~3x) for cascade event detection.

Clarification of spectral features across 1 TeV to 100 PeV energies.

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.