Mono-Z Dark Matter Search with Neural Spline Flows Using CMS Run 2015D Open Data

Abstract

We report a search for dark matter (DM) produced in association with a leptonically decaying $Z$ boson at $\sqrt{s}=13$ TeV using CMS Run 2015D open data corresponding to an integrated luminosity of $2.32{\mathrm{fb}}^{-1}$ together with simplified-model Monte Carlo simulation. Events are selected in the mono-$Z\to {\ell }^{+}{\ell }^{-}$ final state in both the $\mu \mu$ and $ee$ channels. Forty kinematic observables are extracted from MINIAOD and MINIAODSIM, cleaned with physics-motivated selections, and reduced to a 37-dimensional feature vector. Five Neural Spline Flows are trained independently to model Standard Model background and mediator-specific DM signal densities. The per-event test statistic is constructed from the log-likelihood ratio between the signal and background density estimates, providing sensitivity across the full kinematic phase space without requiring a hard upper $\mathrm{MET}$ threshold. A simultaneous profile-likelihood fit combining the two channels yields observed (expected) 95\% confidence level upper limits on the signal-strength parameter of $\mu <0.0177$ ($0.0018$) for the scalar mediator, $\mu <0.0362$ ($0.0039$) for the vector mediator, and $\mu <0.0498$ ($0.0069$) for the axial-vector mediator. The observed limits are weaker than expected because of a residual high-$\mathrm{MET}$ background-modeling discrepancy rather than evidence for a DM signal. To our knowledge, this is the first application of Neural Spline Flow likelihood-ratio scoring to a mono-$Z$ dark matter search using CMS Run 2015D open data simultaneously in the $\mu \mu$ and $ee$ channels.