Graph Neural Networks for low-energy event classification & reconstruction in IceCube
Journal of Instrumentation IOP Publishing 17:11 (2022) P11003-P11003
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 fieldPanScales parton showers for hadron collisions: formulation and fixed-order studies
Journal of High Energy Physics Springer Nature 2022:11 (2022) 19
PanScales showers for hadron collisions: all-order validation
Journal of High Energy Physics Springer Nature 2022:11 (2022) 20
Ising Machines for Diophantine Problems in Physics
Fortschritte Der Physik 70:11 (2022)
Abstract:
Diophantine problems arise frequently in physics, in for example anomaly cancellation conditions, string consistency conditions and so forth. We present methods to solve such problems to high order on annealers that are based on the quadratic Ising Model. This is the intrinsic framework for both quantum annealing and for common forms of classical simulated annealing. We demonstrate the method on so-called Taxicab numbers (discovering some apparently new ones), and on the realistic problem of anomaly cancellation in U(1) extensions of the Standard Model.Searches for Neutrinos from Gamma-Ray Bursts Using the IceCube Neutrino Observatory
Astrophysical Journal 939:2 (2022)