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1. Graph Neural Networks for low-energy event classification & reconstruction in IceCube

   https://doi.org/10.1088/1748-0221/17/11/P11003  

   Abbasi, R.; Ackermann, M.; Adams, J.; Aggarwal, N.; Aguilar, J.A.; Ahlers, M.; Ahrens, M.; Alameddine, J.M.; Alves, A.A.; Amin, N.M.; et al (November 2022, Journal of Instrumentation)

   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 to current IceCube methods. Alternatively, the GNN offers a reduction of the background (i.e. false positive) rate by over a factor 8 (to below half a percent) at a fixed signal efficiency. For the reconstruction of energy, direction, and interaction vertex, the resolution improves by an average of 13%–20% compared to current maximum likelihood techniques in the energy range of 1 GeV–30 GeV. The GNN, when run on a GPU, is capable of processing IceCube events at a rate nearly double of the median IceCube trigger rate of 2.7 kHz, which opens the possibility of using low energy neutrinos in online searches for transient events. 

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2. Cosmic ray measurements with IceCube and IceTop

   https://doi.org/10.21468/SciPostPhysProc.13.002  

   Soldin, Dennis (January 2023, SciPost Physics Proceedings)

   IceCube is a cubic-kilometer Cherenkov detector in the deep ice at the geographic South Pole. The dominant event yield in the deep ice detector consists of penetrating atmospheric muons with energies above approximately 300 GeV, produced in cosmic ray air showers. In addition, the surface array, IceTop, measures the electromagnetic component and GeV muons of air showers. Hence, IceCube and IceTop yield unique opportunities to study cosmic rays with unprecedented statistics in great detail. We will present recent results of comic ray measurements from IceCube and IceTop. In this overview, we will highlight measurements of the energy spectrum of cosmic rays from 250 TeV up to the EeV range and their mass composition above 3 PeV. We will also report recent results from measurements of the muon content in air showers and discuss their consistency with predictions from current hadronic interaction models. 

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3. Detection of astrophysical tau neutrino candidates in IceCube

   https://doi.org/10.1140/epjc/s10052-022-10795-y  

   IceCube_Collaboration; Abbasi, R.; Ackermann, M.; Adams, J.; Aguilar, J_A; Ahlers, M.; Ahrens, M.; Alispach, C.; Alves, Jr., A_A; Amin, N_M; et al (November 2022, The European Physical Journal C)

   Abstract High-energy tau neutrinos are rarely produced in atmospheric cosmic-ray showers or at cosmic particle accelerators, but are expected to emerge during neutrino propagation over cosmic distances due to flavor mixing. When high energy tau neutrinos interact inside the IceCube detector, two spatially separated energy depositions may be resolved, the first from the charged current interaction and the second from the tau lepton decay. We report a novel analysis of 7.5 years of IceCube data that identifies two candidate tau neutrinos among the 60 “High-Energy Starting Events” (HESE) collected during that period. The HESE sample offers high purity, all-sky sensitivity, and distinct observational signatures for each neutrino flavor, enabling a new measurement of the flavor composition. The measured astrophysical neutrino flavor composition is consistent with expectations, and an astrophysical tau neutrino flux is indicated at 2.8$$\sigma $$ $\sigma$significance. 

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4. New approaches for gamma-hadron separation at the IceCube Neutrino Observatory

   https://doi.org/10.1051/epjconf/202328004004  

   Bontempo, Federico (January 2023, EPJ Web of Conferences)

   Capone, A.; De Vincenzi, M.; Morselli, A. (Ed.)

   The IceCube Neutrino Observatory located at the geographic South Pole is composed of two detectors. One is the in-ice optical array, which measures high-energy muons from air showers and charged particles produced by the interaction of high-energy neutrinos in the ice. The other is an array of ice-Cherenkov tanks at the surface, called IceTop, which is used both as veto for the in-ice neutrino measurements and for detecting cosmic-ray air showers. In the next decade, the IceCube-Gen2 extension will increase the surface coverage including surface radio antennas and scintillator panels on the footprint of an extended optical array in the ice. The combination of the current surface and in-ice detectors can be exploited for the study of cosmic rays and the search for PeV gamma rays. The in-ice detector measures the high-energy muonic component of air showers, whereas the signal in IceTop is dominated by the electromagnetic component. The relative size of the muonic and electromagnetic components is different for gamma-and hadron-induced air showers. Thus, the gamma-hadron separation of cosmic rays is attempted using machine learning techniques including deep learning. Here, different approaches are presented. Finally, the prospects for the detection of PeV photons with IceCube-Gen2 will be discussed. 

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5. Low energy event classification in IceCube using boosted decision trees

   https://doi.org/10.1088/1748-0221/16/12/C12007  

   Leonard DeHolton, K. (December 2021, Journal of Instrumentation)

   Abstract The DeepCore sub-array within the IceCube Neutrino Observatory is a densely instrumented region of Antarctic ice designed to observe atmospheric neutrino interactions above 5 GeV via Cherenkov radiation. An essential aspect of any neutrino oscillation analysis is the ability to accurately identify the flavor of neutrino events in the detector. This task is particularly difficult at low energies when very little light is deposited in the detector. Here we discuss the use of machine learning to perform event classification at low energies in IceCube using a boosted decision tree (BDT). A BDT is trained using reconstructed quantities to identify track-like events, which result from muon neutrino charged current interactions. This new method improves the accuracy of particle identification compared to traditional classification methods which rely on univariate straight cuts. 

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