Abstract:
In recent years novel inference techniques have been developed based on the construction of non-linear summary statistics with neural networks by minimising inference- motivated losses. One such technique is inferno which was shown on toy problems to outperform classical summary statistics for the problem of confidence interval estimation in the presence of nuisance pa- rameters. In order to test and benchmark the algorithm in a real world application, a full, systematics-dominated analysis produced by the CMS experiment, "Measurement of the t ̄t production cross section in the τ+jets channel in pp collisions at s = 7 TeV (CMS Collaboration, The European Physical Journal C, 2013) is reproduced with CMS Open Data. The application of the inferno-powered neural network architecture to this analysis demonstrates the potential to reduce the impact of systematic uncertainties in real LHC analyses. This work also exemplifies the extent to which LHC analyses can be reproduced with open data. https://arxiv.org/abs/2301.10358
ZOOM DETAILS: https://uci.zoom.us/j/97010614727