Abstract The IceCube Neutrino Observatory is designed to observe neutrinos interacting deep within the South Pole ice sheet. It consists of 5160 digital optical modules, which are arrayed over a cubic kilometer from 1450 m to 2450 m depth. At the lower center of the array is the DeepCore subdetector. It has a denser configuration which lowers the observable energy threshold to about 10 GeV and creates the opportunity to study neutrino oscillations with low energy atmospheric neutrinos. A precise reconstruction of neutrino direction is critical in the measurements of oscillation parameters. In this contribution, I will discuss a method to reconstruct the zenith angle of 10-GeV scale events in IceCube using a convolutional neural network and compare the result to that of the current likelihood-based reconstruction algorithm.
This content will become publicly available on November 1, 2023
Graph Neural Networks for low-energy event classification & reconstruction in IceCube
Abstract IceCube, a cubic-kilometer array of optical sensors built to detect atmospheric and astrophysical neutrinos between 1 GeV and 1 PeV, is deployed 1.45 km to 2.45 km below the surface of the ice sheet at the South Pole. The classification and reconstruction of events from the in-ice detectors play a central role in the analysis of data from IceCube. Reconstructing and classifying events is a challenge due to the irregular detector geometry, inhomogeneous scattering and absorption of light in the ice and, below 100 GeV, the relatively low number of signal photons produced per event. To address this challenge, it is possible to represent IceCube events as point cloud graphs and use a Graph Neural Network (GNN) as the classification and reconstruction method. The GNN is capable of distinguishing neutrino events from cosmic-ray backgrounds, classifying different neutrino event types, and reconstructing the deposited energy, direction and interaction vertex. Based on simulation, we provide a comparison in the 1 GeV–100 GeV energy range to the current state-of-the-art maximum likelihood techniques used in current IceCube analyses, including the effects of known systematic uncertainties. For neutrino event classification, the GNN increases the signal efficiency by 18% at a fixed background rate, compared more »
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De Mitri, I. ; Barbato, F.C.T. ; Boncioli, D. ; Evoli, C. ; Pagliaroli, G. ; Salamida, F. (Ed.)The IceCube Neutrino Observatory is a multi-component detector at the South Pole. Besides studying high-energy neutrinos, it is capable of measuring high-energy cosmic rays from PeV to EeV. This energy region is thought to cover the transition from galactic to extragalactic sources of cosmic rays. The observatory consists of the deep in-ice IceCube array, which measures the high-energy (≥500 GeV) muonic component, and the IceTop surface array, which is sensitive to the electromagnetic and low-energy muonic part of an air shower. The primary energy and the mass composition can be measured simultaneously by applying statistical methods including modern machine-learning techniques to reconstruct cosmic ray air showers. In this contribution, we will discuss recent improvements to the reconstruction techniques, the mass composition sensitivity, and an outlook on future improved measurements with the full surface scintillator/radio array to mitigate snow accumulation and measure the air shower maximum X max using imaging air-Cherenkov telescopes IceAct.
The IceCube Neutrino Observatory at the geographic South Pole consists of two components, a km2 surface array IceTop and a km3 in-ice array between 1.5 and 2.5 km below the surface. Cosmic ray events with primary energy above a few tens of TeV may trigger both the IceTop and in-ice array and leave a three-dimensional footprint of the electromagnetic and muonic components in the extensive air shower. A new reconstruction based on the minimization of a unified likelihood function involving quantities measured by both IceTop and in-ice detectors was developed. This report describes the new reconstruction algorithm and summarizes its performance tested with Monte Carlo events under two different containment conditions. The advantages of the new reconstruction are discussed in comparison with reconstructions that use IceTop or in-ice data separately. Some possible improvements are also summarized.