https://phys.org/news/2018-11-machine-accuracy-particle-identification-lhc.html

The Large Hadron Collider beauty experiment (LHCb) studies unstable particles called B-mesons. Their decays demonstrate the clearest asymmetry between matter and antimatter. The LHCb consists of several specialised detectors, specifically, calorimeters to measure the energy of neutral particles. Calorimeters also identify different types of particles. These are done by search and analysis of corresponding clusters of energy deposition. It is, however, not easy to separate signals from two types of photons—primary photons and photons from energetic π0 meson decay. HSE scientists developed a method that to classify these two with high accuracy.

The authors of the study applied artificial neural networks and gradient boosting (a machine-learning algorithm) to classify energies collected in the individual cells of the energy cluster.