Mass agnostic jet taggers
Searching for new physics in large data sets needs a balance between
two competing effects—signal identification vs background distortion. In
this work, we perform a systematic study of both single variable and
multivariate jet tagging methods that aim for this balance. The methods
preserve the shape of the background distribution by either augmenting
the training procedure or the data itself. Multiple quantitative metrics
to compare the methods are considered, for tagging 2-, 3-, or 4-prong
jets from the QCD background. This is the first study to show that the
data augmentation techniques of Planing and PCA based scaling deliver
similar performance as the augmented training techniques of Adversarial
NN and uBoost, but are both easier to implement and computationally
cheaper.