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Resonance Searches with Machine Learned Likelihood Ratios

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arxiv 2002.04699 v1 pith:QP2KDHKS submitted 2020-02-11 hep-ph

Jacob Hollingsworth , Daniel Whiteson This is my paper
classification hep-ph
keywords likelihoodratiolearnedcalculateddrawnmachineratiosresonance
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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.

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