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arxiv: 2604.25604 · v1 · submitted 2026-04-28 · ✦ hep-ex

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Search for electroweakinos in compressed-spectrum scenarios with low-momentum isolated tracks in proton-proton collisions at sqrt{s} = 13 TeV

CMS Collaboration

Authors on Pith no claims yet

Pith reviewed 2026-05-07 14:13 UTC · model grok-4.3

classification ✦ hep-ex
keywords supersymmetryelectroweakinoshiggsinocompressed spectrummissing transverse momentumneural networkCMSsoft tracks
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0 comments X

The pith

CMS excludes higgsino charginos up to 185 GeV when mass splitting to the neutralino lies between 0.28 and 1.15 GeV.

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

The paper conducts a dedicated search for supersymmetric electroweakinos in compressed mass spectra, where a chargino and the lightest neutralino differ by only a few hundred MeV. It focuses on proton-proton collision events that contain one low-momentum isolated track, likely from a soft pion produced in the chargino decay, together with large missing transverse momentum carried away by undetected particles. A neural network trained on kinematic and track-impact-parameter variables separates the expected signal from standard-model backgrounds. With 138 fb^{-1} of 13 TeV data recorded by the CMS detector, no statistically significant excess is observed. The analysis therefore places 95% confidence-level exclusions on the higgsino-like model across the stated mass-splitting and chargino-mass ranges, directly limiting natural supersymmetry scenarios that require light, weakly interacting particles.

Core claim

No significant excess above the standard model expectation is observed. The search uses a parameterized neural network that combines kinematic information and track impact parameters to discriminate signal events containing a soft, possibly displaced pion track from background. At 95% confidence level the considered higgsino model is excluded for mass splittings between 0.28 and 1.15 GeV and for chargino masses up to 185 GeV.

What carries the argument

Parameterized neural network that classifies events using kinematic variables together with the impact parameter of low-momentum isolated tracks.

If this is right

  • The lightest chargino in the higgsino model must exceed 185 GeV if the mass splitting to the neutralino is between 0.28 and 1.15 GeV.
  • Natural supersymmetry parameter space with small electroweakino mass differences is reduced.
  • Future LHC runs at higher luminosity can target the remaining allowed region at larger chargino masses or smaller splittings using the same soft-track signature.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The result can be reinterpreted in other supersymmetric models that produce similar soft-track plus missing-momentum signatures.
  • Improved modeling of low-momentum track efficiencies in simulation would directly tighten or loosen the present limits.
  • Complementary searches at future colliders or with different final states would be needed to close the remaining gaps in compressed electroweakino scenarios.

Load-bearing premise

The neural-network discrimination and background modeling in the low-momentum, high-impact-parameter region accurately reflect real data.

What would settle it

A statistically significant excess of events with soft isolated tracks and large missing transverse momentum in the signal regions, beyond the predicted background, would invalidate the no-excess conclusion and the resulting exclusion limits.

Figures

Figures reproduced from arXiv: 2604.25604 by CMS Collaboration.

Figure 1
Figure 1. Figure 1: For ∆m0 ≳ 1 GeV, low-momentum (soft) leptons from electroweakino decays can be observed [15, 16]. For ∆m± ≲ 0.3 GeV, the chargino lifetime is sufficiently long to yield a dis￾appearing track signature [17–19]. The intermediate range, 0.3–1 GeV, is particularly difficult to probe, as the chargino decays primarily to a single soft pion with a displacement of up to about 1 cm from the primary vertex (PV) [20]… view at source ↗
Figure 2
Figure 2. Figure 2: Distributions of example kinematic track observables for data and simulation. The view at source ↗
Figure 3
Figure 3. Figure 3: The expected and observed yields as a function of the 0.3 GeV (upper left), 0.6 GeV view at source ↗
Figure 4
Figure 4. Figure 4: The expected and observed data yields in the 12 SRs (left), where the uncertainties view at source ↗
read the original abstract

A search for supersymmetric electroweakinos is performed using events with a low-momentum (soft) isolated track and large missing transverse momentum, targeting nearly mass-degenerate higgsino-like charginos and neutralinos. For mass splittings of 0.3$-$1 GeV, the chargino decays to the lightest neutralino and a low-momentum pion, which can produce a soft, potentially displaced track. A parameterized neural network separates signal from background using kinematic and impact parameter information. The analysis uses 138 fb$^{-1}$ of proton$-$proton collision data at a center-of-mass energy of 13 TeV recorded with the CMS detector. No significant excess above the standard model expectation is observed. At 95% confidence level, the considered higgsino model is excluded for mass splittings in the range 0.28$-$1.15 GeV and for chargino masses up to 185 GeV, setting stringent constraints on natural supersymmetry scenarios.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The manuscript presents a search for supersymmetric electroweakinos in compressed mass spectra using proton-proton collisions at 13 TeV with the CMS detector and an integrated luminosity of 138 fb^{-1}. The analysis focuses on events with a low-momentum isolated track and large missing transverse momentum, targeting the decay of nearly degenerate charginos to neutralinos and soft pions. A parameterized neural network is used to separate potential signal from standard model backgrounds based on kinematic and impact parameter information. No significant excess above the expected background is observed, leading to 95% confidence level exclusions on higgsino-like models for mass splittings between 0.28 and 1.15 GeV and chargino masses up to 185 GeV.

Significance. If the background modeling and neural network discrimination hold, this result would be significant for constraining natural supersymmetry scenarios, particularly the compressed higgsino region that is motivated by electroweak naturalness but difficult to probe due to soft decay products. The approach extends sensitivity to small mass splittings using soft tracks and machine learning techniques. The analysis follows standard CMS practices for limit setting and reports no excess, providing direct data-driven constraints.

major comments (1)
  1. The central exclusion limits depend on the parameterized neural network's ability to accurately model the background in the low-momentum, high-impact-parameter tail. The manuscript should provide explicit validation of the NN output distribution and background prediction using data in control regions that closely match the signal region's kinematics and pile-up conditions, as mismatches in soft-track reconstruction efficiency or impact-parameter resolution could shift the expected background and alter the exclusion contours.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their thorough review and constructive feedback on our manuscript. We address the major comment below and have revised the manuscript accordingly.

read point-by-point responses

  1. Referee: The central exclusion limits depend on the parameterized neural network's ability to accurately model the background in the low-momentum, high-impact-parameter tail. The manuscript should provide explicit validation of the NN output distribution and background prediction using data in control regions that closely match the signal region's kinematics and pile-up conditions, as mismatches in soft-track reconstruction efficiency or impact-parameter resolution could shift the expected background and alter the exclusion contours.

    Authors: We agree that detailed validation of the neural network performance in relevant control regions is crucial for the robustness of the background modeling. In the revised version of the manuscript, we have added explicit comparisons of the NN output distributions in data and simulation for control regions that closely mimic the signal region kinematics, including variations in pile-up conditions. These control regions are defined using events with lower missing transverse momentum or by inverting certain selection criteria while maintaining similar track properties. We also discuss the systematic uncertainties associated with soft-track reconstruction efficiency and impact parameter resolution, demonstrating that any potential mismatches are covered by the assigned uncertainties and do not significantly alter the exclusion limits. The new validation plots and corresponding text have been incorporated into Section 7 and the supplementary material. revision: yes

Circularity Check

0 steps flagged

No circularity: experimental limit derived directly from data with independent background estimation

full rationale

The analysis uses 138 fb^{-1} of recorded CMS data to search for soft displaced tracks from electroweakino decays, applying a parameterized neural network trained on simulation to discriminate signal from SM backgrounds in kinematic and impact-parameter variables. No excess is observed relative to the standard-model expectation, yielding 95% CL exclusions on higgsino mass splittings and chargino masses. The background modeling and NN output rely on simulation validated in control regions outside the signal region; no parameter is fitted to the signal-region data and then presented as a prediction, no self-citation supplies a uniqueness theorem or ansatz, and the final limit is not equivalent to any input by construction. The derivation chain is therefore self-contained against external data.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The central claim rests on standard-model background simulation, detector response modeling, and the assumption that the signal Monte Carlo correctly describes the soft-pion kinematics and track displacement. No new particles or forces are invented; the only free parameters are the neural-network hyperparameters and the signal-strength fit parameter.

free parameters (2)
  • neural-network hyperparameters
    Chosen to optimize signal-background separation; their values are not reported in the abstract.
  • signal-strength scaling factor
    Fitted in the limit-setting procedure; the exclusion contour is defined where this factor is excluded at 95% CL.
axioms (2)
  • domain assumption Standard Model background processes and their cross sections are correctly modeled by simulation in the soft-track region.
    Invoked when subtracting backgrounds from the observed data.
  • domain assumption Detector simulation accurately reproduces tracking efficiency and impact-parameter resolution for pions below 1 GeV.
    Required for signal acceptance and background estimation.

pith-pipeline@v0.9.0 · 5480 in / 1442 out tokens · 50817 ms · 2026-05-07T14:13:59.976635+00:00 · methodology

discussion (0)

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Reference graph

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