Mass-decorrelated jet tagging in ATLAS

Mass-decorrelated jet substructure observables have the potential to increase the sensitivity of searches for new physics in final states with high-pT hadronically decaying resonances, by minim- ising the sculpting of background jet mass distributions and enabling more robust background estimation. A comprehensive study of different analytical and multivariate mass-decorrelation techniques is performed in Monte Carlo simulation: designed decorrelated taggers (DDT), fixed-efficiency k-NN regression, convolved substructure (CSS), adversarially trained neural networks (ANN), and adaptive boosting for uniform efficiency (uBoost) are studied and compared. Performance is evaluated using metrics for background rejection and mass-decorrelation.