Flavor at FASER: Discovering Light Scalars Beyond Minimal Flavor Violation

TL;DR

This paper explores flavored scalar models with couplings aligned to Yukawa matrices, highlighting D-meson decays as a key production channel and demonstrating FASER2's potential to probe these models, offering insights into flavor physics beyond minimal flavor violation.

Contribution

It introduces a class of flavored scalar models with non-minimal flavor violation and analyzes FASER2's sensitivity to these particles through meson decay channels.

Findings

FASER2 can probe scalar production from D and B meson decays.

Scalar couplings are aligned with Yukawa matrices, with small flavor-violating entries.

Results are model-independent, based on meson branching fractions and scalar lifetime/mass.

Abstract

We study a simple class of flavored scalar models, in which the couplings of a new light scalar to standard-model fermions are controlled by the flavor symmetry responsible for fermion masses and mixings. The scalar couplings are then aligned with the Yukawa matrices, with small but nonzero flavor-violating entries. $D$-meson decays are an important source of scalar production in these models, in contrast to models assuming minimal flavor violation, in which $B$ and $K$ decays dominate. We show that FASER2 can probe large portions of the parameter space of the models, with comparable numbers of scalars from $B$ and $D$ decays in some regions. If discovered, these particles will not only provide evidence of new physics, but they may also shed new light on the standard model flavor puzzle. Finally, the richness of theoretical models underscores the importance of model-independent interpretations. We therefore analyze the sensitivity of FASER and other experimental searches in terms of physical parameters:~(i) the branching fractions of heavy mesons to the scalar, and (ii) $\tau/m$, where $\tau$ and $m$ are the scalar's lifetime and mass, respectively. The results are largely independent of the new particle's spin and can be used to extract constraints on a wide variety of models.