STUDY OF FEASIBILITY AND USEFULNESS OF MACHINE LEARNING METHODS TO HELP IDENTIFYING RESIDUAL GAS COMPOSITION

This report summarizes the outcome of the collaboration between CERN and Intelligent Data Analysis Laboratory (IDAL). In this feasibility study we investigated the potential usefulness of machine-learning applications to identify residual gas compositions. The report focus on the performance of the most promising machine-learning method that have been put in place during work package WP3 of the project.