First event-by-event Cherenkov separation from sub-MeV electrons in liquid argon enables a proof-of-principle ALP search excluding new parameter space despite no observed excess.
IceCube -- Neutrinos in Deep Ice The Top 3 Solutions from the Public Kaggle Competition
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abstract
During the public Kaggle competition "IceCube -- Neutrinos in Deep Ice", thousands of reconstruction algorithms were created and submitted, aiming to estimate the direction of neutrino events recorded by the IceCube detector. Here we describe in detail the three ultimate best, award-winning solutions. The data handling, architecture, and training process of each of these machine learning models is laid out, followed up by an in-depth comparison of the performance on the kaggle datatset. We show that on cascade events in IceCube above 10 TeV, the best kaggle solution is able to achieve an angular resolution of better than 5 degrees, and for tracks correspondingly better than 0.5 degrees. These performance measures compare favourably to the current state-of-the-art in the field.
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First Demonstration of a Hybrid Cherenkov and Scintillation Detector in a Proof-of-Principle Axion Search at a Beam Dump
First event-by-event Cherenkov separation from sub-MeV electrons in liquid argon enables a proof-of-principle ALP search excluding new parameter space despite no observed excess.
- Enhancing event reconstruction for $\gamma$-ray particle detector arrays using transformers