1–4 Nov 2022
Rutgers University
US/Eastern timezone

Truth tagging for efficiency parametrization of b-jets using Graph Neural Networks

3 Nov 2022, 10:00
20m
Multipurpose Room (aka Livingston Hall) (Rutgers University)

Multipurpose Room (aka Livingston Hall)

Rutgers University

Livingston Student Center

Speaker

Krunal Bipin Gedia (ETH Zurich (CH))

Description

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 in arXiv:2004.02665.

Primary author

Krunal Bipin Gedia (ETH Zurich (CH))

Presentation materials