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In recent years novel inference techniques have been developed based on theconstruction of non-linear summary statistics with neural networks byminimising inferencemotivated losses. One such technique is inferno (P. deCastro and T. Dorigo, Comp. Phys. Comm. 244 (2019) 170) which was shown on toyproblems to outperform classical summary statistics for the problem ofconfidence interval estimation in the presence of nuisance parameters. In orderto test and benchmark the algorithm in a real world application, a full,systematics-dominated analysis produced by the CMS experiment, "Measurement ofthe top-antitop production cross section in the tau+jets channel in ppcollisions at sqrt(s) = 7 TeV" (CMS Collaboration, The European PhysicalJournal C, 2013) is reproduced with CMS Open Data. The application of theinferno-powered neural network architecture to this analysis demonstrates thepotential to reduce the impact of systematic uncertainties in real LHCanalyses. This work also exemplifies the extent to which LHC analyses can bereproduced with open data.

Application of Inferno to a Top Pair Cross Section Measurement with CMS Open Data