Machine Learning based Reconstruction for the MUonE Experiment

Abstract

As currently operating high energy physics experiments produce a huge amount of data, new methods of fast and efficient event reconstruction are necessary to handle the immense load. Storing the unprocessed data is not feasible, forcing experiments to process the data online employing the algorithms of quality provided for the offline analysis, but within strict time constraints. In the MUonE experiment the machine learning based event reconstruction techniques are being implemented and tested in order to provide efficient online reduction of data and to maximize the statistical power of the final physics measurement.