# Constraining the SMEFT with Bayesian reweighting

## TL;DR

This paper demonstrates how Bayesian reweighting can efficiently incorporate new experimental data into SMEFT analyses, validated through a top quark sector study, and compares its effectiveness with traditional fitting methods.

## Contribution

It introduces a validated Bayesian reweighting approach for SMEFT, showing its equivalence to new fits under certain conditions and quantifying information gain.

## Key findings

- Reweighting matches new fit results for sensitive operators.
- Quantifies information gain using Shannon entropy and KS statistic.
- Analyzes dependence on different weight expressions.

## Abstract

We illustrate how Bayesian reweighting can be used to incorporate the constraints provided by new measurements into a global Monte Carlo analysis of the Standard Model Effective Field Theory (SMEFT). This method, extensively applied to study the impact of new data on the parton distribution functions of the proton, is here validated by means of our recent SMEFiT analysis of the top quark sector. We show how, under well-defined conditions and for the SMEFT operators directly sensitive to the new data, the reweighting procedure is equivalent to a corresponding new fit. We quantify the amount of information added to the SMEFT parameter space by means of the Shannon entropy and of the Kolmogorov-Smirnov statistic. We investigate the dependence of our results upon the choice of either the NNPDF or the Giele-Keller expressions of the weights.