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WavePID: low-energy flavor identification using single-PMT time series in IceCube
Journal article   Peer reviewed

WavePID: low-energy flavor identification using single-PMT time series in IceCube

IceCube collaboration, R Abbasi, M Ackermann, J. Adams, J. A Aguilar, M Ahlers, J. M Alameddine, S Ali, N. M Amin and M Hostert
Journal of instrumentation, Vol.21(9), P09023
09/01/2026
DOI: 10.1088/1748-0221/21/09/P09023

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Abstract

The IceCube Neutrino Observatory, a cubic-kilometer detector at the South Pole, identifies neutrino flavor through event morphology. Sparse photon detection makes this classification particularly challenging in the 5–100 GeV regime, the energy range relevant for oscillation measurements and searches for physics beyond the Standard Model. We introduce WavePID, a template-based log-likelihood-ratio classifier that exploits nanosecond-scale timing on individual detector modules through three observables: the distance to the reconstructed vertex, the early-charge fraction, and the module-to-module time difference. Evaluated on a cascade-enriched sample selected by a state-of-the-art graph neural network, WavePID improves both cascade purity and classification performance over the neural network alone. This demonstrates that per-module pulse timing carries flavor-identification information complementary to morphology-based classifiers, opening a new physics-motivated observable for low-energy neutrino reconstruction. Geant4 simulations associate this signal with differences in Cherenkov emission geometry between muon tracks and electromagnetic showers. These results motivate exploiting nanosecond-scale pulse timing in future low-energy classifiers and in detector designs with improved per-module timing in next-generation neutrino telescopes.
 Morphology Telescopes Classification Flavor (particle physics) Graph neural networks Modules Neural networks Neutrinos Sensors South Pole Standard model (particle physics)

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