Speaker
Description
Precise measurements of $B$ meson branching fractions are essential both for testing Standard Model predictions and for many measurements that rely on accurate modeling of data composition in flavor-physics analyses. At Belle II, $B$ mesons are produced via the process $e^+e^- \to \Upsilon(4S) \to B\bar{B}$. The total number of such events can be determined with high precision, yet using this quantity for $B$ meson branching fraction measurements requires knowledge of the production fractions of neutral and charged B meson pairs in $\Upsilon(4S)$ decays, respectively $f_{00}$ and $f_{\pm}$. Although strong isospin symmetry predicts equal production rates, recent theoretical studies indicate a possible energy dependence of the ratio $R^{\pm0}=f_{\pm}/f_{00}$, motivating an experimental investigation. Reconstructing specific $B$ decay channels restricts the analysis to a small fraction of the available $B\bar{B}$ dataset, motivating an inclusive, machine-learning-based approach that exploits the full dataset without channel-specific assumptions.
We present a measurement of $R^{\pm0}$ using a neural network classifier trained on event-level observables to separate $B^+B^-$ from $B^0\bar{B}^0$ events without reconstructing either $B$ meson explicitly. A simultaneous three-class classifier and a two-stage binary architecture, which first suppresses background before separating the two $B \bar{B}$ event types, were both explored to address the limitations of the classification task. $R^{\pm0}$ is extracted from the classifier output through a binned template fit. We discuss the classifier design, the systematic effects relevant to its application on data, and the outlook for extracting the energy dependence of $R^{\pm0}$ from Belle II data.