Flavour Anomalies: A comparative analysis using a machine learning algorithm

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 $R_{J/ψ}$ and branching ratios that exhibit tensions with Standard Model predictions on $B \rightarrow K^{(*)} ν\barν$ 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.

Figures (12)

• Figure 1: Fit to the WET parameters to the $R_{D^*}$ and $R_{J/\psi}$ ratios at $1\, \sigma$ (purple), $2\, \sigma$ (blue), $3\, \sigma$ (green) and $4\, \sigma$ (yellow).
• Figure 2: Fit to the WET parameters for $B\to K^{(*)}\nu\bar{\nu}$ branching ratios. The teal areas correspond to $\mathrm{BR}(B^+\to K^+\nu \bar{\nu}) = (2.3\pm0.7)\times10^{-5}$ and the purple area to $\mathrm{BR}(B^0 \to K^{*0}\nu\bar{\nu}) < 1.8\times 10^{-5}$, while the red areas are the best fit at $1\,\sigma$ and $2\,\sigma$.
• Figure 3: Negative of the logarithm of the likelihood function when varying (a) $C_1$ (b) $C_3$ and (c) $\beta^q$ with respect to their values at the best fit for Scenario III.
• Figure 4: Regions of constant likelihood in Scenario III for (a) $C_1$ and $C_3$ plane (b) $C_1$ and $\beta^q$ plane and (c) $C_3$ and $\beta^q$ plane, with the rest of parameters as in the best fit point of Scenario III.
• Figure 5: Pull of the observables considered for the global fit in the SM (orange line) and Scenario III (blue line). The observables are ordered by decreasing SM pull, and the ones in which the SM and Scenario III differ more than $1\,\sigma$ are specially marked.