What Machine Learning Can Do for Focusing Aerogel Detectors
This addresses data processing challenges for particle identification in the Super Charm-Tau factory experiment, but appears incremental as it applies existing ML techniques to a specific detector issue.
The paper tackled the problem of filtering ambient background hits in the Focusing Aerogel Ring Imaging CHerenkov detector to reduce data flow and improve particle velocity resolution, presenting several machine learning-inspired approaches for signal hit filtering.
Particle identification at the Super Charm-Tau factory experiment will be provided by a Focusing Aerogel Ring Imaging CHerenkov detector (FARICH). The specifics of detector location make proper cooling difficult, therefore a significant number of ambient background hits are captured. They must be mitigated to reduce the data flow and improve particle velocity resolution. In this work we present several approaches to filtering signal hits, inspired by machine learning techniques from computer vision.