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

(2) Mohan Babu University; Amaravati; Bhavishya Chebrolu (2) ((1) VIT-AP University; Hitesh Rasineni (1); India; India); Tirupati

REVIEW 4 major objections 5 minor 29 references

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

T0 review · 4 major / 5 minor · reviewed 2026-08-02 · deepseek-v4-flash

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Pith's one-line read Using neural-spline-flow likelihood-ratio scores on public 2015 proton-proton collision data, this analysis sets observed 95% upper limits on the dark matter signal strength for three mediator models, and attributes the 7–12x gap between ob

desk verdict A careful, honest open-data study whose central limits are undercut by the authors' own demonstration that the background model fails in the high-MET tail. read the letter →

arxiv 2607.13771 v1 pith:MLR4TZBU submitted 2026-07-15 cs.LG hep-ex

Hitesh Rasineni (1) , Bhavishya Chebrolu (2) ((1) VIT-AP University , Amaravati , India , (2) Mohan Babu University , Tirupati , India) This is my paper · ORCID

classification cs.LGhep-ex

keywords darkmattermono-Zneuralsplineflowslikelihoodratiomissingtransversemomentumprofileopendatadensityestimation

topics Dark Matter

open problems Dark Matter

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper aims to establish that a mono-Z dark matter search can be run end-to-end on public 2015 collision data using a learned density-ratio score instead of hand-crafted variables. Five neural spline flows—two background flows trained on control-region events with missing transverse momentum below 50 GeV for the muon and electron channels, and three signal flows trained on mediator Monte Carlo—produce the per-event score as the log-density difference between signal and background. A simultaneous signal-plus-validation profile-likelihood fit yields observed 95% upper limits on the signal-strength parameter of 0.0177 (scalar), 0.0362 (vector), and 0.0498 (axial-vector), with expected limits roughly 7–12 times smaller. The paper's central interpretive claim is that the gap is driven by a high-MET (≥100 GeV) background-shape residual, not by evidence for dark matter, and it documents that neither linear nor quadratic extrapolations predict that tail.

What carries the argument

The engine is the per-event log-likelihood-ratio score formed from two Neural Spline Flow density estimates: S_h(x) = log p(x|DM_h) − log p(x|SM_channel). A Neural Spline Flow is a normalizing flow whose coupling transforms are monotonic rational-quadratic splines, giving exact tractable densities. Background flows are trained only on control-region events (MET < 50 GeV), one per lepton channel; signal flows are trained on simulated mediator events and evaluated in the same standardized SM feature domain. The score arrays feed a binned profile-likelihood fit over signal and validation regions (MET 50–100 GeV as a sideband) with per-channel normalization nuisances, and asymptotic CL_s formula

What would settle it

Build a background flow that includes events from an independent high-MET sideband (MET between 100 and 200 GeV) and repeat the signal-region fit. If the fitted signal strength drops toward zero and observed limits approach expected limits, the high-MET tail residual was the cause; if the positive signal strength persists with high q0, the residual is intrinsic to the flow/density modeling or to the signal model rather than a CR-to-SR transfer artifact.

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Extended reading notes

Core claim

On its own terms, the paper claims that a likelihood-ratio test statistic built from independently trained neural spline flows can serve as the full discriminant for a mono-Z dark matter search, removing the need for a hard upper cut on missing transverse momentum. In the signal region (MET ≥ 50 GeV, |Δφ(MET, Z)| > 2.5, ≤ 1 jet), the per-event score S(x) = log p(x|signal) − log p(x|background), formed from three mediator-specific signal flows and two channel-specific background flows, is fed into a simultaneous signal-region plus validation-region binned profile likelihood. The result is observed 95% CL upper limits on the signal strength μ of <0.0177 (scalar), <0.0362 (vector), and <0.0498

Load-bearing premise

The background score distribution in the signal region—especially the rare events with missing transverse momentum above 100 GeV—is correctly described by a background density trained on MET < 50 GeV events and shifted by one normalization step; the paper's own validation shows simple extrapolations fail in that tail, so if this assumption fails the quoted limits are not valid constraints.

Editorial extensions

If this is right

The density-ratio score concentrates sensitivity across the full phase space, so the search does not rely on a hard upper MET threshold; events up to the dataset's ~200 GeV MET cap enter the fit.
A validation-region sideband can supply a data-driven background normalization constraint of roughly 0.85% per channel, even on a small 2.32 fb^-1 dataset.
The observed-to-expected ratio remains ~7–12 across three mediator hypotheses and three background variants, identifying the high-MET tail as the dominant systematic rather than the signal model.
The pipeline is transferable to other final states or signal hypotheses by retraining the relevant signal and background flows on the same public data.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Inference: Because early stopping never triggered and validation negative log-likelihood was still decreasing at the final epoch, the five flows are likely undertrained; retraining to convergence could change both signal/background separation and the shape of the high-MET tail.
Inference: The failure of linear and quadratic extrapolations suggests the background density should be MET-conditioned (e.g., a flow with MET as a conditioning input) to distinguish an extrapolation artifact from a genuine high-MET background population.
Inference: On a larger dataset, this single-step VR-to-SR shape transfer would likely become the limiting systematic before statistical gains arrive, so a dedicated high-MET control sample or a closure-based reweighting would be needed.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

You should know this paper is an honest piece of work that nonetheless fails to support its headline result. The authors train neural spline flows to score events in a mono-Z dark-matter search on CMS open data, and they report 95% CL upper limits on signal strength. But their own validation shows the background template breaks exactly where the signal would appear, so the limits are not reliable. The reader's REJECT verdict is right, and the stress-test note lands: Section 7.3 shows the CR-trained flow does not extrapolate to the high-MET tail, with linear and quadratic models missing the tail mean by 90–115 and 300–330 score units. Section 7.2 then explains that this residual drives the 7–12x inflation of observed versus expected limits. The central numbers in Table 2 come from a background model the paper itself proves is wrong in the signal region. That is a load-bearing flaw, not a minor caveat.

Credit where it is due: the paper is unusually transparent. It ships a detailed pipeline, uses public data and MC, defines regions clearly, and explicitly quantifies the failure rather than hiding it. The literature review is fair and cites the relevant density-ratio methods (ANODE, CATHODE, SALAD) and the full Run 2 CMS search. The authors also flag the undertraining of the flows and the missing MET/pileup systematics. As a methodological write-up, it is a solid example of how to document a failed extrapolation in a likelihood-ratio search.

The soft spots are severe, though. The single-step VR→SR shape transfer is the backbone of the fit, and it does not close in the tail. The scalar cross-section limit is absurdly small (1.76e-9 pb), which signals a normalization issue. The axial-vector sample merges two different benchmark masses, making the physical interpretation murky. And the per-channel normalization nuisance of 5% cannot cover a shape shift of this size. The paper's own diagnosis is that the observed limits are inflated by an unmodeled residual, which is another way of saying the limits are not valid constraints.

For a reader: this is not a paper whose physics numbers you should quote. It is a cautionary tale about density-ratio methods in sparse tails, and the honest failure analysis could be useful methodologically. I would bring it to a reading group as a case study, but I would not cite the limits myself.

Should it go to peer review? Yes, I think so. It is a serious, reproducible attempt with a clearly articulated problem, and a good referee could either guide a major revision (e.g., using a more robust background model) or provide a definitive rejection with useful feedback. It deserves more than a desk reject.

Referee Report

4 major / 5 minor

Summary. The paper presents a machine-learning search for dark matter produced in association with a leptonically decaying Z boson, using CMS Run 2015D open data and simplified-model Monte Carlo. Neural Spline Flows are trained on SM control-region data and on three mediator-specific signal MC samples; a per-event log-likelihood-ratio score is built and combined in a simultaneous SR+VR binned profile-likelihood fit. The authors report observed (expected) 95% CL upper limits on the signal strength for scalar, vector, and axial-vector mediators, e.g. mu < 0.0177 (0.0018), and note explicitly that the observed limits are weaker than expected due to a high-MET background-modeling residual. Crucially, the paper's own validation in Section 7.3 shows that the CR-trained NSF density does not extrapolate reliably into the SR tail, and Section 7.2 attributes the large q0 and the 7-12x observed-to-expected ratios to this unresolved residual. Yet Table 2 quotes limits derived from exactly that background model.

Significance. If the limits were valid, this would constitute a novel application of Neural Spline Flow likelihood-ratio scoring to a mono-Z dark matter search using open CMS data, with a plausible claim to methodological novelty and a detailed reproducible pipeline. The paper is unusually transparent: it provides region definitions, fit configurations, numerical JSON outputs, and an explicit, quantified account of its own background-modeling failures. These strengths are real and should be credited. However, the central numerical claim is not supported: the background template used to set the limits is shown by the authors themselves to fail precisely in the signal-like high-MET tail, and the expected-limit/expected-band pairs in the result tables are internally inconsistent. As a physics search, the manuscript cannot stand. The reproducible pipeline might be of interest as a methods-only study, but the physics limit claim as written is not.

major comments (4)

[Sections 7.2-7.3, Eq. (2), Table 2] The background model used for the limits is the single-step VR-to-SR shape transfer described in Section 7 and Eq. (2). Section 7.3's own validation shows that the CR-trained NSF density does not extrapolate to the high-MET SR tail: linear and quadratic extrapolations of the mean score miss the true SR-tail mean by 90-115 and 300-330 score units, respectively. Section 7.2 states that the 160-181 tail events (MET>=100 GeV) carry mean scores 140-195 units above the VR bulk, where the VR template has negligible support. The profile-likelihood fit then absorbs this shape discrepancy as signal, producing q0 values of 233-327 and observed/expected limit ratios of roughly 7-12. Since the quoted 95% CL limits in Table 2 are derived from exactly this unvalidated background model, their coverage is not established. This is an internal inconsistency between the validation results and the central cl
[Table 2 and Appendices E.2, E.4, E.5] The reported expected limits and expected bands are mutually inconsistent. In Table 2, the scalar expected limit is mu95_exp = 0.0018, while the 95% expected band is [0.00154, 0.00166]; the vector expected limit 0.0039 is above the quoted 95% band [0.00309, 0.00382]. The same pattern appears in Appendix E.4 (scalar 0.0018 vs [0.00145, 0.00176]) and Appendix E.5. In an asymptotic CLs calculation, the median expected limit must lie inside the central 68% interval and certainly inside the 95% interval. These numbers therefore cannot all be correct, indicating a procedural or computational error in the limit pipeline that directly affects the central results.
[Section 6 (NSF training and early stopping)] The manuscript states that for all five flows, validation NLL continued to decrease at epoch 200 and early stopping was never triggered; every checkpoint is therefore the final epoch rather than a converged minimum. The authors flag this as 'potential residual undertraining'. This is not a minor caveat: the per-event scores are the entire basis of the likelihood-ratio test statistic, and underfit densities will distort the score distribution, particularly in the sparse high-MET tail where the analysis' main difficulty lies. The paper does not quantify the effect of non-convergence on the reported limits. Without converged flows or a convergence study, the learned densities are not a reliable foundation for the quoted numerical results.
[Section 3, Table 5, Table 2 (axial-vector signal model)] The axial-vector signal sample combines two physically distinct benchmarks: M_chi=10 GeV, M_V=20 GeV (sigma=1.856 pb) and M_chi=50 GeV, M_V=200 GeV (sigma=0.158 pb). A single Neural Spline Flow is trained on the combined sample, and the same fitted signal strength mu is then converted into separate cross-section limits for each benchmark in Table 2. The signal density used in the likelihood is thus a mixture of two different spectra with different kinematics and different cross sections; it is not the density for either benchmark individually. The resulting cross-section limits are therefore not interpretable as constraints on either benchmark point. This should either be treated as two separate hypotheses or as a mixture with a well-defined composition; as written, the formalism is not well defined.

minor comments (5)

[Eq. (1)] The notation p(x|SM_ell ell) leaves the channel dependence implicit. Since the two SM flows are trained on different channels, the score should explicitly indicate the channel, e.g. S_h^{(ee)} and S_h^{(mu mu)}.
[Table 14 vs. Eq. (2)] The fit configuration lists 'Statistical method: chi2 asymptotic CLs approximation,' while Eq. (2) defines a binned Poisson likelihood. The relationship between the Poisson likelihood and the chi2 approximation used for the scans should be clarified.
[Section 6, footnote after training protocol] The footnote about the significance cap (Z=8.0 due to floating-point underflow) is placed in the middle of the training protocol. It belongs in the results section, and the q0 values themselves should be reported with their numerical precision.
[Figure 2 caption] The caption says 'Pre-unblinding validation-region score distributions,' but no unblinding procedure or decision rule is described anywhere. Please clarify the blinding protocol or reword the caption.
[Section 7.1] MET resolution and pileup systematics are explicitly not propagated. Given that the signal region and the dominant background residual are defined by MET, even a rough estimate of these effects is necessary; as written, the systematic budget is incomplete and this gap should be acknowledged more prominently in the conclusions.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the CR-trained SM density, independent DM MC, and VR-sideband profile likelihood form a self-contained derivation chain; the documented high-MET residual is a validation failure, not a definitional or self-citation reduction.

full rationale

The derivation chain is not circular. The SM NSF density is trained exclusively on CR events with MET < 50 GeV (Sec. 6); the three DM densities are trained on separate MonoZToLL MC samples (Sec. 3); the per-event score (Eq. 1) is the log-density ratio between these independently trained flows; and the final limits come from a binned profile-likelihood fit (Eq. 2) in which the SR background template is obtained by a single-step VR to SR shape transfer: 'the SM VR score histogram is renormalised to the respective region yield and used as the nominal background prediction for that region' (Sec. 7). This is a sideband transfer, not a fitted parameter renamed as a prediction, and Appendix E.3 explicitly states: 'This approach avoids a pure SR-direct circularity while keeping the SR score shape as the primary discriminant.' The signal-strength mu is not an input to either flow and is not forced by the signal-model choice, since the signal MC is independent of the background data. There are no load-bearing self-citations: the cited CMS/ATLAS results, theory papers, and CERN open-data records are external, and the 'first application' claim is not used to justify the limits. The paper's own validation (Secs. 7.2-7.3 and App. E.4) shows that the CR-trained density does not extrapolate to the high-MET tail: 'Neither functional form reliably extrapolates the CR-trained NSF density into the high-MET SR tail,' and the VR-extrapolated template 'does not close in the high-score tail.' This is a genuine background-modelling and validation failure that undermines the quoted limits as physics constraints, but it is not a circular construction: the mismatch is measured against the same external SR data rather than being generated by the model. Other noted limitations (possible NSF undertraining in Sec. 6; unpropagated MET-resolution and pileup systematics in Sec. 7.1) likewise affect robustness, not circularity. Accordingly, no circular step is identified.

Assumptions & free parameters 5 free parameters · 6 assumptions · 0 invented entities

The paper introduces no new particles or interactions. Its free parameters are the fitted signal strength, per-channel normalizations, and a set of hand-chosen or data-derived choices (architecture, cleaning caps, binning). The key axioms are domain assumptions about background representativeness and flow accuracy, both of which are explicitly challenged by the paper's own validation results.

free parameters (5)

signal strength mu = scalar 0.0177, vector 0.0362, axial-vector 0.0498 (observed 95% CL upper limits)
The signal-strength multiplier is the primary fitted parameter in the profile-likelihood fit; its fitted value is inflated by the background residual.
per-channel normalization nuisances theta_mumu, theta_ee = constrained by VR data, posterior uncertainty ~0.17 sigma
Nuisance parameters absorbing normalization mismodeling in each channel.
NSF hyperparameters = 8 transforms, 8 bins, 2x256 hidden units, lr schedule, 200 epochs
Hand-chosen architecture and training settings; no hyperparameter search is reported, and early stopping never triggered, so these choices directly affect the learned densities.
SM feature cleaning caps = e.g., met pt cap 200 GeV, hadronic recoil cap 564 GeV, derived from data quantiles
Data-derived tail caps used to clip features; these are not physics constants and shape the input domain for both SM and DM flows.
score histogram binning = 30 initial bins merged to min 20 background events
Bin choices affect the binned likelihood and resulting limits.

assumptions (6)

domain assumption Drell-Yan Z+jets events in the control region (MET<50 GeV) are representative of the background shape in the signal region.
The SM flows are trained only on CR events; Section 7.3 shows the extrapolation to SR fails.
domain assumption The MonoZToLL simulated samples correctly model the kinematics of the simplified-model DM mediators.
Signal flows are trained on these MC samples, and the same samples provide the signal templates in the fit.
domain assumption Normalizing flows can accurately approximate the 37-dimensional event densities from the available training data.
The method relies on the NSF density estimates being reliable; the paper flags residual undertraining.
standard math Asymptotic formulae for the CLs method apply to the binned likelihood fits.
The paper uses Cowan et al. [23] with a chi-square approximation; no coverage checks are shown.
domain assumption The single-step VR->SR shape transfer provides a valid nominal background prediction.
The VR histogram is renormalised to SR yield; the paper itself shows this transfer does not close in the high-score tail.
domain assumption The luminosity and lepton efficiency uncertainty values from external CMS measurements apply to this analysis.
These are propagated from literature values, not derived here.

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Cite this review

Pith. "Pith review of Mono-Z Dark Matter Search with Neural Spline Flows Using CMS Run 2015D Open Data." pith.science (2026). https://pith.science/paper/MLR4TZBU

@misc{pith2026260713771,
  author       = {Pith},
  title        = {Pith review of: Mono-Z Dark Matter Search with Neural Spline Flows Using CMS Run 2015D Open Data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MLR4TZBU}},
  note         = {Machine review of arXiv:2607.13771}
}

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-\(Z\rightarrow\ell^+\ell^-\) 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.

Figures

Figures reproduced from arXiv: 2607.13771 by the authors.

**Figure 1.** Control-region distributions of log p(x | NSF SM) for the electron (left) and muon (right) channels. Overlaid Gaussian fits summarise the location and width of the dominant peak. 6 [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗

**Figure 2.** Pre-unblinding validation-region score distributions comparing observed data with [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

**Figure 3.** Post-fit SR score distributions in the electron (top row) and muon (bottom row) [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

Figures from the paper (6 more)

**Figure 4.** Figure 4: Signal-region NSF score distributions for scalar, vector, and axial-vector mediator [PITH_FULL_IMAGE:figures/full_fig_p011_4.png]

**Figure 5.** Figure 5: Asymptotic CLs scans for the scalar, vector, and axial-vector mediator hypotheses from the simultaneous SR+VR fit. The VR constrains the background normalisation; the SR provides the signal search sensitivity. The horizontal dashed line marks the 95% CL exclusion thres…

**Figure 6.** Figure 6: CLs scans for the VR-extrapolated validation fit. Observed CLs crosses the 95% threshold for all three mediators once the scan range brackets ˆµ; see [PITH_FULL_IMAGE:figures/full_fig_p024_6.png]

**Figure 7.** Figure 7: Post-fit score distributions for the VR-extrapolated validation fit. The signal-plus [PITH_FULL_IMAGE:figures/full_fig_p025_7.png]

**Figure 8.** Figure 8: Asymptotic CLs scans for the scalar, vector, and axial-vector mediator hypotheses from the SR-direct fit. The horizontal dashed line marks the 95% CL exclusion threshold; vertical lines indicate the observed and expected upper limits on µ. 26 [PITH_FULL_IMAGE:figures/…

**Figure 9.** Figure 9: Post-fit SR score distributions in the electron (top row) and muon (bottom row) [PITH_FULL_IMAGE:figures/full_fig_p027_9.png]

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Works this paper leans on

29 extracted references · 3 canonical work pages

[1]

Neural spline flows.Advances in Neural Informa- tion Processing Systems, 32, 2019

Conor Durkan, Artur Bekasov, Iain Mur- ray, and George Papamakarios. Neural spline flows.Advances in Neural Informa- tion Processing Systems, 32, 2019

2019
[2]

Normaliz- ing flows for probabilistic modeling and inference.J

George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan. Normaliz- ing flows for probabilistic modeling and inference.J. Mach. Learn. Res., 22:1–64, 2021

2021
[3]

Jessica Goodman, Masahiro Ibe, Arvind Rajaraman, William Shepherd, Tim M. P. Tait, and Hai-Bo Yu. Constraints on dark matter from colliders.Phys. Rev. D, 82:116010, 2010

2010
[4]

Bell, Ahmad J

Nicole F. Bell, Ahmad J. Galea, James B. Dent, Thomas D. Jacques, Lawrence M. Krauss, and Thomas J. Weiler. Searching for dark matter at the LHC with a mono-Z. Phys. Rev. D, 86:096011, 2012

2012
[5]

Carpenter, Andrew Nelson, Chase Shimmin, Tim M

Linda M. Carpenter, Andrew Nelson, Chase Shimmin, Tim M. P. Tait, and Daniel Whiteson. Collider searches for dark matter in events with a Z boson and missing energy.Phys. Rev. D, 87:074005, 2013

2013
[6]

Dark matter benchmark models for early LHC run-2 searches: Report of the AT- LAS/CMS dark matter forum.arXiv preprint, 2015

ATLAS and CMS Collaborations. Dark matter benchmark models for early LHC run-2 searches: Report of the AT- LAS/CMS dark matter forum.arXiv preprint, 2015. Editors: Antonio Boveia and Caterina Doglioni

2015
[7]

Searches for dark matter at the LHC: A multivariate analysis in the mono-Z channel.Phys

Alexandre Alves and Kuver Sinha. Searches for dark matter at the LHC: A multivariate analysis in the mono-Z channel.Phys. Rev. D, 92:115005, 2015

2015
[8]

Search for new physics in events with a leptonically decaying Z boson and a large transverse momentum imbalance in proton–proton collisions at√s = 13 TeV.Phys

CMS Collaboration. Search for new physics in events with a leptonically decaying Z boson and a large transverse momentum imbalance in proton–proton collisions at√s = 13 TeV.Phys. Rev. D, 97:092005, 2018

2018

Show all 29 references

[9]

Search for dark mat- ter produced in association with a leptoni- cally decaying Z boson in proton–proton collisions at √s = 13 TeV.Eur

CMS Collaboration. Search for dark mat- ter produced in association with a leptoni- cally decaying Z boson in proton–proton collisions at √s = 13 TeV.Eur. Phys. J. C, 81:13, 2021

2021
[10]

mono-Z ′: searches for dark matter in events with a resonance and missing transverse energy

Marcelo Autran, Kevin Bauer, Tongyan Lin, and Daniel Whiteson. mono-Z ′: searches for dark matter in events with a resonance and missing transverse energy. Phys. Rev. D, 92:115014, 2015

2015
[11]

Fox, Roni Harnik, Graham D

Wolfgang Altmannshofer, Patrick J. Fox, Roni Harnik, Graham D. Kribs, and Nir- mal Raj. Dark matter signals in dilepton production at hadron colliders.Phys. Rev. D, 91:015005, 2015

2015
[12]

Probing the dark sector through mono-Z boson leptonic decays.Phys

Daneng Yang and Qiang Li. Probing the dark sector through mono-Z boson leptonic decays.Phys. Rev. D, 97:015022, 2018

2018
[13]

Elgammal

S. Elgammal. Angular distribution study for high mass dimuon pairs in CMS open 2012 data and for mono-Z ′ model.arXiv preprint, 2024

2012
[14]

Testing the boundaries: Normaliz- ing flows for higher dimensional data sets

Humberto Reyes-Gonz´ alez and Riccardo Torre. Testing the boundaries: Normaliz- ing flows for higher dimensional data sets. SciPost Phys., 13:047, 2022

2022
[15]

Event generation and den- sity estimation with surjective normalizing flows.SciPost Phys., 13:047, 2022

Rob Verheyen. Event generation and den- sity estimation with surjective normalizing flows.SciPost Phys., 13:047, 2022. See arXiv:2205.01697 for publication details

2022 arXiv
[16]

Simulation assisted likelihood-free anomaly detection.Phys

Anders Andreassen, Benjamin Nachman, and David Shih. Simulation assisted likelihood-free anomaly detection.Phys. Rev. D, 101:055004, 2020

2020
[17]

DoubleMuon pri- mary dataset in MINIAOD format from RunD of 2015 (/DoubleMuon/Run2015D- 16Dec2015-v1/MINIAOD)

CMS Collaboration. DoubleMuon pri- mary dataset in MINIAOD format from RunD of 2015 (/DoubleMuon/Run2015D- 16Dec2015-v1/MINIAOD). CERN Open Data Portal, Record 24127, 2021. DOI: 10.7483/OPENDATA.CMS.H3TX.ZJZX

2015 doi
[19]

Simulated dataset DarkMatter MonoZToLL V Mx-1 Mv- 500 gDMgQ-1 TuneCUETP8M1 13TeV- madgraph in MINIAODSIM format for 2015 collision data

CMS Collaboration. Simulated dataset DarkMatter MonoZToLL V Mx-1 Mv- 500 gDMgQ-1 TuneCUETP8M1 13TeV- madgraph in MINIAODSIM format for 2015 collision data. CERN Open Data Portal, Record 16630, 2021. DOI: 10.7483/OPENDATA.CMS.YTLH.E0N7

2015 doi
[20]

Simulated dataset DarkMatter MonoZToLL A Mx-10 Mv- 20 gDMgQ-1 TuneCUETP8M1 13TeV- madgraph in MINIAODSIM format for 2015 collision data

CMS Collaboration. Simulated dataset DarkMatter MonoZToLL A Mx-10 Mv- 20 gDMgQ-1 TuneCUETP8M1 13TeV- madgraph in MINIAODSIM format for 2015 collision data. CERN Open Data Portal, Record 16575,

2015
[21]

Simulated dataset DarkMatter MonoZToLL A Mx-50 Mv- 200 gDMgQ-1 TuneCUETP8M1 13TeV- madgraph in MINIAODSIM format for 2015 collision data

CMS Collaboration. Simulated dataset DarkMatter MonoZToLL A Mx-50 Mv- 200 gDMgQ-1 TuneCUETP8M1 13TeV- madgraph in MINIAODSIM format for 2015 collision data. CERN Open Data Portal, Record 16597, 2021. DOI: 10.7483/OPENDATA.CMS.34IE.KN6I

2015 doi
[22]

Sim- ulated dataset DarkMat- ter MonoZToLL EWK Scalar Mx- 100 Lambda- 3000 TuneCUETP8M1 13TeV-madgraph in MINIAODSIM format for 2015 collision data

CMS Collaboration. Sim- ulated dataset DarkMat- ter MonoZToLL EWK Scalar Mx- 100 Lambda- 3000 TuneCUETP8M1 13TeV-madgraph in MINIAODSIM format for 2015 collision data. CERN Open Data Portal, Record 16601, 2021. DOI: 10.7483/OPEN- DATA.CMS.NW7F.NFGG

2015 doi
[23]

Asymptotic formulae for likelihood-based tests of new physics.Eur

Glen Cowan, Kyle Cranmer, Eilam Gross, and Ofer Vitells. Asymptotic formulae for likelihood-based tests of new physics.Eur. Phys. J. C, 71:1554, 2011

2011
[24]

Pseudoscalar portal dark matter.Phys

Asher Berlin, Stefania Gori, Tongyan Lin, and Lian-Tao Wang. Pseudoscalar portal dark matter.Phys. Rev. D, 92:015005, 2015

2015
[25]

Seng Pei Liew, Michele Papucci, Alessan- dro Vichi, and Kathryn M. Zurek. Mono-X versus direct searches: Simplified models for dark matter at the LHC.JHEP, 03:100, 2017

2017
[26]

Search for high- mass new phenomena in the dilepton fi- nal state using proton–proton collisions at √s = 13 TeV with the ATLAS detector

ATLAS Collaboration. Search for high- mass new phenomena in the dilepton fi- nal state using proton–proton collisions at √s = 13 TeV with the ATLAS detector. Phys. Lett. B, 761:372–392, 2016

2016
[27]

Search for new high-mass phenomena in the dilepton final state using 36 fb −1 of proton–proton colli- sion data at √s = 13 TeV with the ATLAS detector.Phys

ATLAS Collaboration. Search for new high-mass phenomena in the dilepton final state using 36 fb −1 of proton–proton colli- sion data at √s = 13 TeV with the ATLAS detector.Phys. Lett. B, 776:318–338, 2018

2018
[28]

Anomaly detection with density estima- tion.Phys

Benjamin Nachman and David Shih. Anomaly detection with density estima- tion.Phys. Rev. D, 101:075042, 2020

2020
[29]

Classifying anomalies through outer density estimation.Phys

Anna Hallin et al. Classifying anomalies through outer density estimation.Phys. Rev. D, 106:055006, 2022

2022
[30]

Precision luminosity measurement in proton-proton collisions at √s = 13 TeV in 2015 and 2016 at cms

CMS Collaboration. Precision luminosity measurement in proton-proton collisions at √s = 13 TeV in 2015 and 2016 at cms. Eur. Phys. J. C, 81:800, 2021. 29

2015

Pith tools

Reviewed August 2, 2026 · model on record in the stance chip above.

[1] [1]

Neural spline flows.Advances in Neural Informa- tion Processing Systems, 32, 2019

Conor Durkan, Artur Bekasov, Iain Mur- ray, and George Papamakarios. Neural spline flows.Advances in Neural Informa- tion Processing Systems, 32, 2019

2019

[2] [2]

Normaliz- ing flows for probabilistic modeling and inference.J

George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan. Normaliz- ing flows for probabilistic modeling and inference.J. Mach. Learn. Res., 22:1–64, 2021

2021

[3] [3]

Jessica Goodman, Masahiro Ibe, Arvind Rajaraman, William Shepherd, Tim M. P. Tait, and Hai-Bo Yu. Constraints on dark matter from colliders.Phys. Rev. D, 82:116010, 2010

2010

[4] [4]

Bell, Ahmad J

Nicole F. Bell, Ahmad J. Galea, James B. Dent, Thomas D. Jacques, Lawrence M. Krauss, and Thomas J. Weiler. Searching for dark matter at the LHC with a mono-Z. Phys. Rev. D, 86:096011, 2012

2012

[5] [5]

Carpenter, Andrew Nelson, Chase Shimmin, Tim M

Linda M. Carpenter, Andrew Nelson, Chase Shimmin, Tim M. P. Tait, and Daniel Whiteson. Collider searches for dark matter in events with a Z boson and missing energy.Phys. Rev. D, 87:074005, 2013

2013

[6] [6]

Dark matter benchmark models for early LHC run-2 searches: Report of the AT- LAS/CMS dark matter forum.arXiv preprint, 2015

ATLAS and CMS Collaborations. Dark matter benchmark models for early LHC run-2 searches: Report of the AT- LAS/CMS dark matter forum.arXiv preprint, 2015. Editors: Antonio Boveia and Caterina Doglioni

2015

[7] [7]

Searches for dark matter at the LHC: A multivariate analysis in the mono-Z channel.Phys

Alexandre Alves and Kuver Sinha. Searches for dark matter at the LHC: A multivariate analysis in the mono-Z channel.Phys. Rev. D, 92:115005, 2015

2015

[8] [8]

Search for new physics in events with a leptonically decaying Z boson and a large transverse momentum imbalance in proton–proton collisions at√s = 13 TeV.Phys

CMS Collaboration. Search for new physics in events with a leptonically decaying Z boson and a large transverse momentum imbalance in proton–proton collisions at√s = 13 TeV.Phys. Rev. D, 97:092005, 2018

2018