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

We report a search for dark matter (DM) produced in association with a leptonically decaying (Z) boson at (√s=13) TeV using CMS Run 2015D open data corresponding to an integrated luminosity of (2.32,fb-1) together with simplified-model Monte Carlo simulation. Events are selected in the mono-(Zrightarrowℓ^+ℓ^-) final state in both the (μμ) 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 (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 (μ<0.0177) ((0.0018)) for the scalar mediator, (μ<0.0362) ((0.0039)) for the vector mediator, and (μ<0.0498) ((0.0069)) for the axial-vector mediator. The observed limits are weaker than expected because of a residual high-(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 (μμ) and (ee) channels.