New Physics Through Flavor Tagging at FCC-ee

TL;DR

This paper proposes an advanced flavor tagging analysis at FCC-ee, significantly improving the precision of flavor observables, enabling sensitive tests of the Standard Model and new physics scenarios, including flavor non-universal interactions and B-meson anomalies.

Contribution

It introduces a machine learning-based flavor tagging method for FCC-ee, enhancing measurement precision of flavor ratios and probing new physics beyond current bounds.

Findings

Up to 100x improvement in measurement precision of flavor ratios.

Enhanced sensitivity to flavor non-universal four-fermion interactions.

Ability to test models explaining B-meson anomalies.

Abstract

Leveraging recent advancements in machine learning-based flavor tagging, we develop an optimal analysis for measuring the hadronic cross-section ratios $R_b$, $R_c$, and $R_s$ at the FCC-ee during its $WW$, $Zh$, and $t\bar{t}$ runs. Our results indicate up to a two-order-of-magnitude improvement in precision, providing an unprecedented test of the SM. Using these observables, along with $R_\ell$ and $R_t$, we project sensitivity to flavor non-universal four-fermion (4F) interactions within the SMEFT, contributing both at the tree level and through the renormalization group (RG). We highlight a subtle complementarity with RG-induced effects at the FCC-ee's $Z$-pole. Our analysis demonstrates significant improvements over the current LEP-II and LHC bounds in probing flavor-conserving 4F operators involving heavy quark flavors and all lepton flavors. As an application, we explore simplified models addressing current $B$-meson anomalies, demonstrating that FCC-ee can effectively probe the relevant parameter space. Finally, we design optimized search strategies for quark flavor-violating 4F interactions.