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.
@article{arxiv.2002.04699,
title = {Resonance Searches with Machine Learned Likelihood Ratios},
author = {Jacob Hollingsworth and Daniel Whiteson},
journal= {arXiv preprint arXiv:2002.04699},
year = {2020}
}