Class Imbalance Techniques for High Energy Physics
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A common problem in high energy physics is extracting a signal from a much larger background. Posed as a classification task, there is said to be an imbalance in the number of samples belonging to the signal class versus the number of samples from the background class. Techniques for learning from imbalanced data are well established in the machine learning community. In this work we provide a brief overview of class imbalance techniques in a high energy physics setting. Two case studies are presented: (1) the measurement of the longitudinal polarization fraction in same-sign WW scattering, and (2) the decay of the Higgs boson to charm-quark pairs. We find a significant improvement in the performance of the machine learning models used in the longitudinal WW study, while no significant improvement in performance is found in the deep learning models tested. Our charm-quark tagger gives a 14 improvement in the background rejection rate.
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