The ABCD method is one of the most widely used data-driven background
estimation techniques in high energy physics. Cuts on two
statistically-independent classifiers separate signal and background into four
regions, so that background in the signal region can be estimated simply using
the other three control regions. Typically, the independent classifiers are
chosen "by hand" to be intuitive and physically motivated variables. Here, we
explore the possibility of automating the design of one or both of these
classifiers using machine learning. We show how to use state-of-the-art
decorrelation methods to construct powerful yet independent discriminators.
Along the way, we uncover a previously unappreciated aspect of the ABCD method:
its accuracy hinges on having low signal contamination in control regions not
just overall, but relative to the signal fraction in the signal region. We
demonstrate the method with three examples: a simple model consisting of
three-dimensional Gaussians; boosted hadronic top jet tagging; and a recasted
search for paired dijet resonances. In all cases, automating the ABCD method
with machine learning significantly improves performance in terms of ABCD
closure, background rejection and signal contamination.Comment: 37 pages, 12 figure
