%0 Journal Article %1 hollingsworth2020resonance %A Hollingsworth, Jacob %A Whiteson, Daniel %D 2020 %K bayesian physics %T Resonance Searches with Machine Learned Likelihood Ratios %U http://arxiv.org/abs/2002.04699 %X 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{hollingsworth2020resonance, abstract = {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.}, added-at = {2020-02-13T15:40:42.000+0100}, author = {Hollingsworth, Jacob and Whiteson, Daniel}, biburl = {https://www.bibsonomy.org/bibtex/2fc22bd56aa865d8e3b8eefbc63d80239/kirk86}, description = {[2002.04699] Resonance Searches with Machine Learned Likelihood Ratios}, interhash = {040f495d2518556a0e3c405870811f41}, intrahash = {fc22bd56aa865d8e3b8eefbc63d80239}, keywords = {bayesian physics}, note = {cite arxiv:2002.04699Comment: 7 pages, 6 figures}, timestamp = {2020-02-13T15:40:42.000+0100}, title = {Resonance Searches with Machine Learned Likelihood Ratios}, url = {http://arxiv.org/abs/2002.04699}, year = 2020 }