Identifying $D$ mesons from Radiative $W$ decays at the Large Hadron Collider

TL;DR

This paper introduces machine learning algorithms to identify $D$ mesons from radiative $W$ decays at the LHC, achieving high efficiency and background suppression, enabling precise measurement of decay branching ratios.

Contribution

The paper develops and demonstrates two machine learning algorithms for $D$ meson identification in $W$ radiative decays, improving detection efficiency and background rejection.

Findings

Achieved 47% efficiency in $D$ meson identification.

Suppressed background by a factor of 100.

Enabled prospective measurement of $W o D_s\gamma$ branching ratio.

Abstract

In this paper we present two machine learning algorithms to identify $D$ mesons produced in a color singlet state from radiative $W$ boson decays at the LHC. The combined network algorithm is able to identify $D$ mesons via its hadronic decays with an efficiency of 47% while suppressing a background of quark and gluon jets by a factor of 100. Using the developed algorithm, we perform a prospective study for the measurement of $\mathcal{B}(W\to D_{s}\gamma)$.