One flow to correct them all: improving simulations in high-energy physics with a single normalising flow and a switch

Abstract

Simulated events are key ingredients in almost all high-energy physics analyses. However, imperfections in the simulation can lead to sizeable differences between the observed data and simulated events. The effects of such mismodelling on relevant observables must be corrected either effectively via scale factors, with weights or by modifying the distributions of the observables and their correlations. We introduce a correction method that transforms one multidimensional distribution (simulation) into another one (data) using a simple architecture based on a single normalising flow with a boolean condition. We demonstrate the effectiveness of the method on a physics-inspired toy dataset with non-trivial mismodelling of several observables and their correlations.

19 pages, 12 figures, Dataset: https://doi.org/10.5281/zenodo.13305706

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• Neural Networks for Full Phase-space Reweighting and Parameter Tuning
• CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows
• DCTRGAN: Improving the Precision of Generative Models with Reweighting
• Measurement of the Higgs boson inclusive and differential fiducial production cross sections in the diphoton decay channel with pp collisions at $\sqrt{s}$ = 13 TeV
• Transport away your problems: Calibrating stochastic simulations with optimal transport
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Cited by in corpus (1)

• Measurements of inclusive and differential Higgs boson production cross sections at $\sqrt{s}$ = 13.6 TeV in the H $\to$ $\gamma \gamma$ decay channel