By 2010, the year that construction was completed, it was clear that the IceCube Neutrino Observatory, a project inspired and led by UW–Madison scientists and engineers, was one of the most amazing international science projects of the last few decades. Since then, scientists all around the world have been screening the data from the detector for traces of very high-energy neutrinos, which could reveal the source and nature of cosmic rays.
Of the billions of events detected per year, IceCube researchers need to filter out the 10 or so particles that reach us from the cosmos. The challenge begins with handling large amounts of data leaving the South Pole and includes reconstructing the signal of the neutrinos interacting in the detector.
A multidisciplinary team at UW–Madison, led by Francis Halzen of WIPAC and Chris Re and Ben Recht from the Computer Sciences faculty, has applied advanced computing approaches for improving IceCube’s detection and track reconstruction methods.
New optimized filters and classical data analysis techniques have been introduced to detect and remove outlier hits before reconstructing the trajectory of the muon in IceCube. The new algorithm results in a 13% gain in the angular resolution of the muon track and a 98% accuracy rate in determining the number of muons in coincident events. The paper has just been submitted to “Nuclear Instruments and Methods in Physics Research Section A”.