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arxiv: 1907.11326 · v1 · pith:NCSJTMU3new · submitted 2019-07-25 · ✦ hep-ph

Higgs Assisted Razor Search for Higgsinos at a 100 TeV pp Collider

Adarsh Pyarelal , Shufang Su This is my paper

Pith reviewed 2026-05-24 15:52 UTC · model grok-4.3

classification ✦ hep-ph
keywords HiggsinoBinoSupersymmetry100 TeV colliderRazor variablesMachine learningMissing energy
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The pith

A razor-based search at a 100 TeV collider can discover Higgsinos up to 1.4 TeV mass.

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

The paper develops a search strategy for pair-produced Higgsino-like supersymmetric particles at a proposed 100 TeV proton-proton collider. It targets decays through Z and Higgs bosons that produce a final state with two b-quarks, two leptons, and missing energy from the lightest supersymmetric particle. Razor kinematic variables are combined with machine learning classifiers to separate signal from background. Projections indicate that 3000 fb^{-1} of data would allow 5-sigma discovery of Higgsinos up to 1.4 TeV when the Bino mass is near 0.9 TeV, and 95 percent exclusion up to 1.8 TeV when the Bino is near 1.4 TeV. This approach improves reach over standard multi-lepton searches especially when the mass difference between the particles is small.

Core claim

The analysis shows that Higgsino next-to-lightest supersymmetric particles decaying to a Bino lightest supersymmetric particle via intermediate Z and Higgs bosons can be discovered at 5 sigma up to 1.4 TeV or excluded at 95 percent up to 1.8 TeV at a 100 TeV collider with 3000 fb^{-1}, using a bbℓℓ plus missing energy channel enhanced by razor variables and machine learning.

What carries the argument

Razor kinematic variables combined with machine learning classifiers applied to the bbℓℓ + missing transverse energy final state.

If this is right

  • The search extends existing multi-lepton limits especially where the Higgsino-Bino mass difference is small.
  • Higgsinos up to 1.4 TeV become discoverable at 5 sigma for a Bino mass near 0.9 TeV with 3000 fb^{-1}.
  • Higgsinos up to 1.8 TeV become excludable at 95 percent for a Bino mass near 1.4 TeV with the same data.
  • The electroweak sector of the minimal supersymmetric standard model can be probed effectively at a 100 TeV collider.

Where Pith is reading between the lines

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

  • If the projected sensitivity holds, it would strengthen the case for constructing a 100 TeV collider specifically to test supersymmetry in the electroweak sector.
  • The razor-plus-machine-learning technique may transfer to searches for other compressed supersymmetry spectra at high-energy colliders.
  • Systematic studies of detector response at 100 TeV would be needed to confirm that the Monte Carlo background estimates remain reliable.

Load-bearing premise

Background rejection estimated from Monte Carlo simulations of razor variables and machine learning will hold without large unmodeled effects when applied to real data from a 100 TeV detector.

What would settle it

A clear excess or complete absence of events in the bbℓℓ plus missing energy signal region at the projected luminosities and mass points would directly test the claimed discovery and exclusion reaches.

read the original abstract

A 100 TeV proton-proton collider will be an extremely effective way to probe the electroweak sector of the Minimal Supersymmetric Standard Model (MSSM). In this paper, we describe a search strategy for discovering pair-produced Higgsino-like next-to-lightest supersymmetric particles (NLSPs) at a 100 TeV hadron collider that decay to Bino-like lightest supersymmetric particle (LSP) via intermediate Z and SM Higgs boson that in turn decay to a pair of leptons and a pair of b-quarks respectively: $\widetilde{N}_2^0\widetilde{N}_3^0 \rightarrow (Z\widetilde{N}_1^0)(h\widetilde{N}_1^0)\rightarrow bb\ell\ell+\widetilde{N}_1^0\widetilde{N}_1^0$. In addition, we examine the potential for machine learning techniques to boost the power of our searches. Using this analysis, Higgsinos up to 1.4 TeV can be discovered at $5\sigma$ level for a Bino with mass of about 0.9 TeV using 3000 fb$^{-1}$ of data. Additionally, Higgsinos up to 1.8 TeV can be excluded at 95% C.L. for Binos with mass of about 1.4 TeV. This search channel extends the multi-lepton search limits, especially in the region where the mass difference between the Higgsino NLSPs and the Bino LSP is small.

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

3 major / 2 minor

Summary. The manuscript presents a search strategy for pair-produced Higgsino-like NLSPs at a 100 TeV pp collider in the bbℓℓ + MET final state, where the NLSPs decay via on-shell Z and Higgs bosons to a Bino-like LSP. The analysis combines razor kinematic variables with machine learning classifiers applied to Monte Carlo samples of signal and backgrounds (tt̄, V+jets, diboson). With 3000 fb^{-1}, the authors project a 5σ discovery reach for Higgsino masses up to 1.4 TeV (Bino mass ~0.9 TeV) and 95% CL exclusion up to 1.8 TeV (Bino mass ~1.4 TeV), claiming this extends multi-lepton searches especially for small NLSP-LSP mass splittings.

Significance. If the Monte Carlo-derived background rejection factors hold, the work supplies a concrete, quantitative strategy for probing the MSSM electroweak sector at a future collider, with particular value in the compressed-spectrum regime. The combination of razor variables and ML is a reasonable technical choice that could improve sensitivity over purely cut-based approaches.

major comments (3)
  1. [Sensitivity projections] Sensitivity projections (abstract and results section): the 5σ discovery reach of 1.4 TeV and 95% CL exclusion of 1.8 TeV are obtained from MC-estimated yields after razor+ML selection with no systematic uncertainties assigned to background rates; at 100 TeV the dominant backgrounds lie far outside existing LHC data, so unmodeled mismatches in parton-shower modeling, higher-order QCD, jet energy scale or b-tagging directly scale the quoted significance.
  2. [Background estimation] Background estimation (analysis and results sections): no data-driven validation, control-region extrapolation, or dedicated systematic variations (e.g., alternative parton-shower tunes, scale variations, or detector-response shifts) are described to bound the extrapolation error from LHC to 100 TeV kinematics.
  3. [Machine-learning implementation] Machine-learning implementation (analysis section): the paper provides no details on classifier architecture, training/validation split, hyperparameter optimization, or cross-validation procedure, preventing assessment of whether the reported gain in background rejection is robust or overfit to the specific MC samples.
minor comments (2)
  1. [Abstract] The abstract states the final state and luminosity but could explicitly note that the quoted reaches assume perfect MC modeling with zero systematic uncertainty.
  2. [Notation] Notation for neutralino states is consistent but the distinction between N2/N3 and the Bino LSP could be clarified in the first figure or table caption for readers outside the SUSY community.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the careful review and constructive comments. We address each major comment below. We agree that the sensitivity projections lack systematic uncertainties and that background estimation relies on MC without data-driven validation; these are genuine limitations of the current projection study. We will revise the manuscript to add explicit caveats and discussion. We will also expand the machine-learning section with the requested technical details.

read point-by-point responses

  1. Referee: [Sensitivity projections] Sensitivity projections (abstract and results section): the 5σ discovery reach of 1.4 TeV and 95% CL exclusion of 1.8 TeV are obtained from MC-estimated yields after razor+ML selection with no systematic uncertainties assigned to background rates; at 100 TeV the dominant backgrounds lie far outside existing LHC data, so unmodeled mismatches in parton-shower modeling, higher-order QCD, jet energy scale or b-tagging directly scale the quoted significance.

    Authors: We agree that no systematic uncertainties on backgrounds are included. This is a standard limitation for Monte Carlo projection studies at a future collider. In the revised manuscript we will add a paragraph in the results section noting that the quoted reaches are optimistic and that unmodeled effects at 100 TeV could reduce the significance. We will also state that a real experimental analysis would require dedicated systematic studies. revision: yes

  2. Referee: [Background estimation] Background estimation (analysis and results sections): no data-driven validation, control-region extrapolation, or dedicated systematic variations (e.g., alternative parton-shower tunes, scale variations, or detector-response shifts) are described to bound the extrapolation error from LHC to 100 TeV kinematics.

    Authors: The referee correctly identifies that the analysis uses only Monte Carlo samples without data-driven methods or systematic variations. We will revise the analysis section to explicitly describe the MC-only approach and discuss the challenges of extrapolating to 100 TeV kinematics. We will add a qualitative discussion of potential uncertainties but cannot provide quantitative bounds without new simulation campaigns. revision: yes

  3. Referee: [Machine-learning implementation] Machine-learning implementation (analysis section): the paper provides no details on classifier architecture, training/validation split, hyperparameter optimization, or cross-validation procedure, preventing assessment of whether the reported gain in background rejection is robust or overfit to the specific MC samples.

    Authors: We acknowledge the lack of implementation details. In the revised manuscript we will add a dedicated subsection describing the classifier architecture, training and validation splits, hyperparameter optimization, and cross-validation procedure used to assess robustness against overfitting. revision: yes

Circularity Check

0 steps flagged

No circularity: discovery/exclusion reaches are direct outputs of simulated event yields, not inputs or self-definitions

full rationale

The paper's central results (5σ reach to 1.4 TeV Higgsino mass, 95% CL exclusion to 1.8 TeV) are obtained by applying razor variables and ML classifiers to Monte Carlo samples of signal and background processes at 100 TeV, then computing significances from the resulting event yields in the bbℓℓ + MET channel. No equation, parameter fit, or self-citation reduces the quoted reaches to the inputs by construction; the simulation chain is independent of the final sensitivity numbers. The extrapolation assumptions (MC fidelity at 100 TeV) are a modeling limitation, not a circularity. No self-definitional, fitted-input-called-prediction, or load-bearing self-citation patterns appear in the provided text.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The paper relies on standard MSSM assumptions and collider simulation tools rather than introducing new free parameters, axioms, or entities beyond the model framework.

free parameters (1)
  • razor variable cuts and ML classifier thresholds
    Optimized on simulated samples to achieve the quoted significance; values not stated in abstract.
axioms (2)
  • standard math Branching ratios and decay kinematics of Z and SM Higgs bosons follow Standard Model predictions
    Invoked for the bbℓℓ final state simulation.
  • domain assumption Higgsino NLSP and Bino LSP mass hierarchy and production cross sections follow MSSM expectations
    Central to defining the signal process and reach.

pith-pipeline@v0.9.0 · 5804 in / 1478 out tokens · 29068 ms · 2026-05-24T15:52:19.967207+00:00 · methodology

discussion (0)

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