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

TL;DR

This paper applies the inferno neural network technique to a CMS top pair cross section measurement, demonstrating potential improvements in handling systematic uncertainties and showcasing the reproducibility of LHC analyses with open data.

Contribution

It adapts the inferno method to a real CMS analysis, highlighting its effectiveness in reducing systematic uncertainties in high-energy physics measurements.

Findings

Inferno improves confidence interval estimation in real data.

Neural network approach reduces systematic uncertainties.

Demonstrates reproducibility of LHC analyses with open data.

Abstract

In recent years novel inference techniques have been developed based on the construction of non-linear summary statistics with neural networks by minimising inferencemotivated losses. One such technique is inferno (P. de Castro and T. Dorigo, Comp. Phys. Comm. 244 (2019) 170) which was shown on toy problems to outperform classical summary statistics for the problem of confidence interval estimation in the presence of nuisance parameters. 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 top-antitop production cross section in the tau+jets channel in pp collisions at sqrt(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.