Cosmic-Ray Composition analysis at IceCube using Graph Neural Networks

The IceCube Neutrino Observatory is a multi-component detector embedded deep within the South-Pole Ice. This proceeding will discuss an analysis from an integrated operation of IceCube and its surface array, IceTop, to estimate cosmic-ray composition. The work will describe a novel graph neural network based approach for estimating the mass of primary cosmic rays, that takes advantage of signal-footprint information and reconstructed cosmic-ray air shower parameters. In addition, the work will also introduce new composition-sensitive parameters for improving the estimation of cosmic-ray composition, with the potential of improving our understanding of the high-energy muon content in cosmic-ray air showers

Cosmic-Ray Composition analysis at IceCube using Graph Neural Networks

The IceCube Neutrino Observatory is a multi-component detector embedded deep within the South-Pole Ice. This proceeding will discuss an analysis from an integrated operation of IceCube and its surface array, IceTop, to estimate cosmic-ray composition. The work will describe a novel graph neural network based approach for estimating the mass of primary cosmic rays, that takes advantage of signal-footprint information and reconstructed cosmic-ray air shower parameters. In addition, the work will also introduce new composition-sensitive parameters for improving the estimation of cosmic-ray composition, with the potential of improving our understanding of the high-energy muon content in cosmic-ray air showers

Non-standard neutrino interactions in IceCube

Non-standard neutrino interactions (NSI) may arise in various types of new physics. Their existence would change the potential that atmospheric neutrinos encounter when traversing Earth matter and hence alter their oscillation behavior. This imprint on coherent neutrino forward scattering can be probed using high-statistics neutrino experiments such as IceCube and its low-energy extension, DeepCore. Both provide extensive data samples that include all neutrino flavors, with oscillation baselines between tens of kilometers and the diameter of the Earth. DeepCore event energies reach from a few GeV up to the order of 100 GeV - which marks the lower threshold for higher energy IceCube atmospheric samples, ranging up to 10 TeV. In DeepCore data, the large sample size and energy range allow us to consider not only flavor-violating and flavor-nonuniversal NSI in the μ−τ sector, but also those involving electron flavor. The effective parameterization used in our analyses is independent of the underlying model and the new physics mass scale. In this way, competitive limits on several NSI parameters have been set in the past. The 8 years of data available now result in significantly improved sensitivities. This improvement stems not only from the increase in statistics but also from substantial improvement in the treatment of systematic uncertainties, background rejection and event reconstruction

IceCube Search for Earth-traversing ultra-high energy Neutrinos

The search for ultra-high energy neutrinos is more than half a century old. While the hunt for these neutrinos has led to major leaps in neutrino physics, including the detection of astrophysical neutrinos, neutrinos at the EeV energy scale remain undetected. Proposed strategies for the future have mostly been focused on direct detection of the first neutrino interaction, or the decay shower of the resulting charged particle. Here we present an analysis that uses, for the first time, an indirect detection strategy for EeV neutrinos. We focus on tau neutrinos that have traversed Earth, and show that they reach the IceCube detector, unabsorbed, at energies greater than 100 TeV for most trajectories. This opens up the search for ultra-high energy neutrinos to the entire sky. We use ten years of IceCube data to perform an analysis that looks for secondary neutrinos in the northern sky, and highlight the promise such a strategy can have in the next generation of experiments when combined with direct detection techniques