Applying ML to identify particles in CERN experiments

Inspiration

What if the whole process in particle detector experiments could be automated: right from initiating the detector systems to inferring from the observations generated with high accuracy

What it does

I have made a project on identifying particles through data recorded in CERN experiments. I have tried to amalgamate Machine Learning with Particle Physics so that gigabytes of data can be passed through these classifiers and no human intervention is needed except for initially training the models.

How I built it

There are six particle types: electron, proton, muon, kaon, pion and ghost. Ghost is a particle with other type than the first five or a detector noise. Different particle types remain different responses in the detector systems or subdetectors. Thre are five systems: tracking system, ring imaging Cherenkov detector (RICH), electromagnetic and hadron calorimeters, and muon system. Particle type can be identified using the responses in the detector systems.