Photomultiplier tubes generate waveforms that reveal the signatures of particles, such as photons or muons. The challenge lies in extracting meaningful information from these signals, a task traditionally performed by humans. Now, machine learning techniques, like boosted decision trees, are being employed to automate this labor-intensive process, transforming the way scientists approach particle detection and analysis.
