  We demonstrate the power of machine-learned likelihood ratios for resonance
searches in a benchmark model featuring a heavy Z' boson. The likelihood ratio
is expressed as a function of multivariate detector level observables, but
rather than being calculated explicitly as in matrix-element-based approaches,
it is learned from a joint likelihood ratio which depends on latent information
from simulated samples. We show that bounds drawn using the machine learned
likelihood ratio are tighter than those drawn using a likelihood ratio
calculated from histograms.
