Comunicazione

# Using Graph Neural Networks to improve flavour-tagging and its modeling for the measurement of $WH$ and $ZH$ production in the $H\rightarrow bb$ and $H\rightarrow cc$ decay channel.

##### Tanasini M.
Lunedì 12/09   14:00 - 19:00   Aula B - Maria Goeppert-Mayer   I - Fisica nucleare e subnucleare
Graph Neural Networks (GNNs) are machine learning algorithms particularly suitable for modeling data with complex topological correlations. In this communication, their application in the measurement of $WH$ and $ZH$ production in the $H\rightarrow bb$ and $H\rightarrow cc$ decay channel is presented. First, a new GNN algorithm which is being developed in ATLAS to identify jets containing $b$-hadrons by representing them as graphs of tracks and silicon hits is illustrated. Then, a GNN-based technique for weighting events according to the probability of the jets being $b$-tagged is presented.