Meta-learning and data augmentation for mass-generalised jet taggers

Understand

Deep neural networks trained for jet tagging are typically specific to a narrow range of transverse momenta or jet masses.

• Given the large phase space that the LHC is able to probe, the potential benefit of classifiers that are effective over a wide range of masses or transverse momenta is significant.
• In this work we benchmark the performance of a number of methods for achieving accurate classification at masses distant from those used in training, with a focus on algorithms that leverage meta-learning.
• We study the discrimination of jets from boosted $Z'$ bosons against a QCD background.