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

arXiv: https://arxiv.org/abs/2607.13771 · HTML: https://mlsanity.com/paper/2607.13771/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 15 Jul 2026.

## Builds on (most cited first, 5 of 5)

- [Normalizing Flows for Probabilistic Modeling and Inference](https://mlsanity.com/paper/1912.02762/) (2019)
- [Neural Spline Flows](https://mlsanity.com/paper/1906.04032/) (2019)
- [Anomaly Detection with Density Estimation](https://mlsanity.com/paper/2001.04990/) (2020)
- [Simulation Assisted Likelihood-free Anomaly Detection](https://mlsanity.com/paper/2001.05001/) (2020)
- [Testing the boundaries: Normalizing Flows for higher dimensional data sets](https://mlsanity.com/paper/2202.09188/) (2022)

## Built on by (most cited first, 2 of 2)

- [Hadronic Mono-Z Dark Matter Sensitivity with Flow Matching on CMS Open Data](https://mlsanity.com/paper/2609.02923/) (2026)
- [Likelihood-Based Unsupervised Anomaly Detection in CMS Dijet Events](https://mlsanity.com/paper/2609.06686/) (2026)

## Abstract (arXiv)

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.