Predicting RPC efficiency using machine learning and deep learning models

The report discusses main steps of retrieving RPC (Resistive Plate Chambers) data of multiple LHC (Large Hadron Collider) runs. The retrieved data contains different metrics that are being analyzed by the help of the main statistical methods. Once the data becomes familiar, different Machine Learning and Deep Learning models are trained to predict the fiducial efficiency of each chamber based on the measurable detector quantities collected. Data of CMS experiment of 2018 is used to train the models. For the work, Python scikit-learn and Keras libraries are used to train the models. The predicted results are then compared against the initial fiducial efficiency values