Constructing mass-decorrelated hadronic decay taggers in ATLAS

A large number of physics processes as seen by the ATLAS experiment manifest as collimated, hadronic sprays of particles known as ‘jets’. Jets originating from the hadronic decay of a massive particle are commonly used in searches for new physics. ATLAS has employed multivariate discriminants for the challenging task of identifying the origin of a given jet. However, such classifiers exhibit strong non-linear correlations with the invariant mass of the jet, complicating many analyses which make use of the mass spectrum. A comprehensive study of different mass-decorrelation techniques is performed with ATLAS simulated datasets, comparing designed decorrelated taggers (DDT), fixed-efficiency $k$-NN regression, convolved substructure (CSS), adversarial neural networks (ANNs), and adaptive boosting for uniform efficiency (uBoost). Performance is evaluated using suitable metrics for classification and mass- decorrelation.