Flavour Anomalies: A comparative analysis using a machine learning algorithm

TL;DR

This paper uses a machine learning Monte Carlo algorithm to analyze flavour anomalies in B-meson decays, providing detailed confidence regions and comparing different new physics scenarios to experimental data.

Contribution

It introduces a machine learning-based framework for high-resolution global fits of flavour anomalies, improving analysis of complex likelihood landscapes.

Findings

The scenario with only second and third generation mixing fits data best.

Machine learning approach effectively captures non-Gaussian likelihood structures.

Comparison shows certain new physics scenarios outperform others in explaining anomalies.

Abstract

We present an analysis on flavour anomalies in semileptonic rare $B$-meson decays using an effective field theory approach and assuming that new physics affects only one generation in the interaction basis and non-universal mixing effects are generated by the rotation to the mass basis. A global fit to experimental data is performed, focusing on LFU ratios $R_{D^{(*)}}$ and $R_{J/\psi}$ and branching ratios that exhibit tensions with Standard Model predictions on $B \rightarrow K^{(*)} \nu \bar{\nu}$ decays. In our analysis, we use a Machine Learning Montecarlo algorithm, a framework that emulates the highly non-Gaussian structure of the likelihood landscape with minimal training cost. This method enables the generation of high-resolution confidence regions and detailed correlation analyses. By comparing three different scenarios, we show that the one that introduces only mixing between the second and third quark generations and no mixing in the lepton sector, as well as independent coefficients for the singlet and triplet four fermion effective operators, provides the best fit to the experimental data. A comparison with previous results is performed. We highlight the key strengths of the Machine Learning framework in our analysis.