Seasonal Variations of the Atmospheric Neutrino Flux measured in IceCube

TL;DR

This paper investigates how seasonal temperature changes in the stratosphere influence the atmospheric neutrino flux measured by IceCube, using a novel machine learning-based spectrum unfolding method to analyze energy spectra from 125 GeV to 10 TeV.

Contribution

It introduces the Dortmund Spectrum Estimation Algorithm (DSEA+), a new machine learning approach for unfolding energy spectra of atmospheric neutrinos, accounting for seasonal variations.

Findings

Seasonal variations affect neutrino flux at energies up to 10 TeV.

The novel unfolding method effectively estimates energy spectra from measured data.

Preliminary results show measurable seasonal differences in the neutrino flux.

Abstract

The IceCube Neutrino Observatory measures high energy atmospheric neutrinos with high statistics. These atmospheric neutrinos are produced in cosmic ray interactions in the atmosphere, mainly by the decay of pions and kaons. The rate of the measured neutrinos is affected by seasonal temperature variations in the stratosphere, which are expected to increase with the energy of the particle. In this contribution, seasonal energy spectra are obtained using a novel spectrum unfolding approach, the Dortmund Spectrum Estimation Algorithm (DSEA+), in which the energy distribution from 125 GeV to 10 TeV is estimated from measured quantities with machine learning algorithms. The seasonal spectral difference to the annual average flux will be discussed based on preliminary results from IceCube's atmospheric muon neutrino data.