Efficiency parametrization of b-tagging classifier using Graph Neural Networks

In high-energy physics experiments, estimating the efficiency of a process using selection cuts is a widely used technique. However, this method is limited by the number of events that could be simulated in the required analysis phase space. A way to improve this sensitivity is to use efficiency weights instead of selecting events by selection cuts. This method of efficiency measurements is called Truth tagging. In this talk, we propose a GNN-based approach for Truth-tagging which provides efficiency estimates parameterized in the multi-dimensional phase for b- tagging classifiers in CMS as firstly studied by the ATLAS Collaboration. [1]