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REVIEW 3 major objections 4 minor 119 references

Jet Substructure Probe on Scalar Leptoquark Models via Top Polarization

T0 review · 3 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This paper claims that scalar leptoquarks decaying to a top quark and a neutrino can be discovered at the HL-LHC in boosted-top-plus-missing-energy events (up to 5.4σ), and that top polarization can then separate the S3 and R2 models at…

desk verdict Solid, transparent MC study with a plausible 5-sigma discovery reach, but the 3-sigma model-discrimination claim rests on an explicitly unvalidated subjet b-tagging assumption. read the letter →

arxiv 2505.16328 v2 pith:D5Z2LRXI submitted 2025-05-22 hep-ph

Anupam Ghosh , Partha Konar , Tousik Samui , Ritesh K. Singh This is my paper
classification hep-ph
keywords scalarleptoquarksS3modelR2toppolarizationjetsubstructureb-subjettaggingboostedjetsHL-LHC
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

This paper asks whether the two leading third-generation scalar leptoquark models, $S_3$ and $R_2$, can be discovered and then told apart at the High-Luminosity LHC. Because the two models couple to the top quark with opposite chirality, the top quarks they produce are polarized differently, and that polarization is imprinted on the energy and direction of the bottom quark inside the boosted top jet. Using Soft Drop grooming, $N$-subjettiness, and a custom $b$-subjet tagger on two large-radius jets plus missing transverse momentum, the analysis reports signal significances of up to $5.39\sigma$ for $S_3$ and $4.83\sigma$ for $R_2$ at a leptoquark mass of 1250 GeV with 3000 fb$^{-1}$. A combined boosted-decision-tree score built from polarization-sensitive variables then excludes one model in favor of the other with significance up to $3.23\sigma$, well beyond what the individual variables $z_b = E_b/E_t$ and $\cos\theta_b$ achieve. If these numbers hold in a real detector, the analysis offers both a discovery channel and a way to identify which leptoquark model Nature chose.

What carries the argument

The machinery is a chain of jet substructure tools applied to the hadronically decaying boosted top quark: fixed-radius or dynamic-radius anti-$k_t$ clustering to form the large-radius jets, Soft Drop grooming to remove soft wide-angle radiation, $N$-subjettiness ($\tau_3/\tau_2$) to identify the three subjets expected inside a top jet, and an impact-parameter-significance $b$-tagging method to pick out which subjet came from the bottom quark. The polarization-sensitive variables are built from that tagged subjet: the energy fraction $z_b = E_b/E_t$ and the cosine of the angle between the $b$-subjet and the top-jet boost direction in the top rest frame, $\cos\theta_b$. The identity that carries the argument is the link between leptoquark chirality and top polarization: $S_3$ decays produce left-handed tops while $R_2$ decays produce right-handed tops, so the bottom quark tends to align with the boost in $S_3$ and against it in $R_2$, shifting both $z_b$ and $\cos\theta_b$ and also changing which jet radius best captures the top jet.

What would settle it

Point the LHC's jet-tagging calibration at boosted top jets: reconstruct the bottom subjet inside large-radius jets in events where one top decays to leptons and the other to jets, so the lepton side fixes the top direction, and compare the tagging efficiency against the narrow-jet curve used in the paper. If real subjet tagging falls below that curve, the $\cos\theta_b$ and $z_b$ distributions would smear and the claimed $3.23\sigma$ model separation would not hold; conversely, a 3000 fb$^{-1}$ run that sees no excess in the two-large-radius-jets plus missing-energy channel at 1250 GeV would contradict the claimed $5.39\sigma$ discovery.

Watch

Extended reading notes

Core claim

The paper establishes the two-large-radius-jets plus missing transverse momentum final state as a viable channel for discovering third-generation scalar leptoquarks, and shows that the same final state can measure the chirality of the leptoquark-top-neutrino coupling through top polarization. For a 1250 GeV leptoquark at 14 TeV with 3000 fb$^{-1}$, the optimized analysis reaches $5.39\sigma$ for $S_3$ (using dynamic-radius clustering with $R_0=0.6$) and $4.83\sigma$ for $R_2$ (using fixed-radius clustering with $R=1.2$). The two models have identical production cross sections and identical decay products, so they can only be separated through observables sensitive to the top-quark chirality: the energy fraction carried by the tagged $b$-subjet, the cosine of the $b$-subjet angle in the top rest frame, and a BDT classifier score that combines substructure variables. A $CL_s$-type profile likelihood test on these variables gives model-exclusion significances of up to $3.23\sigma$ for the combined BDT score, compared with at most about $0.7\sigma$ for $z_b$ and $1.4\sigma$ for $\cos\theta_b$ in the same signal regions.

Load-bearing premise

The whole measurement assumes that the detector can identify the bottom quark inside a fast-moving top-quark jet as reliably as it identifies isolated bottom quarks; if that is not true, the polarization signal weakens.

Editorial extensions

If this is right

  • At 3000 fb$^{-1}$ and 14 TeV, a 1250 GeV $S_3$ leptoquark would be discovered at $5.39\sigma$ with dynamic-radius clustering and an $R_2$ leptoquark at $4.83\sigma$ with fixed-radius clustering, in the all-hadronic two-large-radius-jets plus missing-energy channel.
  • The optimal jet radius itself is a chirality indicator: a smaller radius ($R_0 = 0.6$) is best for $S_3$, while a larger radius ($R = 1.2$) is best for $R_2$, because the two chiralities put the $W$ decay products in different places relative to the bottom quark.
  • The combined BDT classifier score outperforms both single polarization variables in model discrimination, reaching up to $3.23\sigma$ exclusion of $R_2$ in favor of $S_3$, versus $1.4\sigma$ for $\cos\theta_b$ and $0.69\sigma$ for $z_b$.
  • The dynamic-radius anti-$k_t$ algorithm is competitive with fixed-radius clustering, giving the best $S_3$ significance while keeping the effective jet size small for QCD-like backgrounds.
  • The same substructure-tagging and polarization-reconstruction pipeline applies to any search with boosted tops and missing energy, turning a discovery channel into a measurement of top-quark chirality.

Reading between the lines

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

  • A natural next step is to map the discrimination power as a function of leptoquark mass; the robustness of the $3.23\sigma$ separation at lower masses is not guaranteed, because softer boosts will degrade both the substructure reconstruction and the $b$-subjet tag.
  • Since $S_3$ and $R_2$ have identical cross sections and final states, the BDT score that separates them is effectively a chirality meter for the top quark, and the same combined-variable strategy could be reused to measure polarization in any boosted-top search.
  • The quoted significances rest on a probabilistic, parton-matched $b$-subjet tagger; a dedicated subjet-level tagging calibration in a full detector simulation could move the numbers in either direction, so the $5.4\sigma$ and $3.2\sigma$ values are best read as the search's potential rather than a final forecast.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. The paper studies pair production of the third-generation scalar leptoquarks S3 and R2 at the 14 TeV HL-LHC, focusing on the final state with two large-radius top jets and large missing transverse momentum. Using MadGraph5_aMC@NLO + Pythia8 + Delphes simulations, the authors apply soft-drop grooming, N-subjettiness, a custom b-subjet tagging method, and boosted decision trees to separate signal from SM backgrounds. They report signal significances up to 5.39σ (S3, DRAK06) and 4.83σ (R2, AK12), and use polarization-sensitive variables (cos θb, Eb/Et, and a BDT classifier score) with a likelihood-ratio test to distinguish the two models, achieving an exclusion significance of up to 3.23σ (S3 vs R2, DRAK06).

Significance. If the results hold, the paper provides a valuable, ready-to-use search strategy for third-generation scalar leptoquarks in a difficult boosted-top plus MET channel, and a concrete method to measure top-quark polarization from jet substructure to discriminate chiral coupling structures. The analysis is carefully documented: event generation and detector simulation use standard, public tools; NLO cross sections with scale and PDF uncertainties are tabulated; BDT training uses train/test separation; and the fixed-radius versus dynamic-radius comparison is systematic. The paper does not suffer from circularity: the polarization formulas (Eqs. 5.5 and 5.6) are standard theory, the chiral structure of the two models is an input, and no fitted parameter is recycled into a derived claim. The main caveats are that the quoted significances are statistical only, and the b-subjet tagging assumption is not independently validated; both are addressed in the major comments.

major comments (3)
  1. [§3.4, §5.2, Table 7] The b-subjet tagging method applies narrow-jet b-tagging efficiencies and mistag rates (Table 2) to subjets inside large-R jets, and the text explicitly states that no detailed b-subjet tagging analysis was performed ("we did not really perform a detailed analysis of the b-subjet tagging"). The model-discrimination result in Table 7 (CL_R2|S3 = 3.23σ for DRAK06 via the BDT classifier score) depends directly on correctly identifying the b-subjet among the three subjets of the boosted top jet. If subjet tagging underperforms relative to narrow-jet tagging, the polarization-sensitive variables (cos θb, Eb/Et) degrade and the separation power could fall below the quoted significance. Please validate the assumption with a dedicated subjet-tagging performance study, or at minimum provide a robustness scan that varies the b-tagging efficiency and mistag rate over a plausible range and quantifies the impact on the exclusion significance.
  2. [§4.5, §6, Eq. (4.3), Tables 6 and 7] The quoted signal significances and model-discrimination significances are computed with Eq. (4.3) using only statistical uncertainties. No systematic uncertainties from jet energy scale, b-tagging efficiency and mistag rates, background normalization, parton distribution functions, renormalization/factorization scales, or pileup are included. At the HL-LHC luminosity of 3000 fb^-1, systematic uncertainties typically dominate over statistical ones in boosted-top analyses, so the 5.39σ and 3.23σ values should be presented explicitly as statistical-only projections, ideally accompanied by a first estimate of the dominant systematic uncertainties or a discussion of which systematics would most affect the discrimination claim.
  3. [§6, §4.2, Table 3] The jet radius parameter (R for anti-kt, R0 for dynamic radius) is optimized separately for the two signal models (Table 3: R=0.8 for S3, R=1.2 for R2; R0=0.6 for S3, R0=0.8 for R2), and the BDT score threshold is chosen to maximize the signal significance. With eight jet configurations (AK08, AK10, AK12, AK15, DRAK05, DRAK06, DRAK08, DRAK10) and a continuous BDT threshold, the reported significances are the maximum of a scan and should be corrected for the trials factor (look-elsewhere effect), or the paper should explicitly state that the optimization is part of the analysis and quote the envelope of the scan rather than a single optimal value.
minor comments (4)
  1. [§5.1, Eq. (5.3)] Equation (5.3) is described as a 'CLs-based profile likelihood estimator,' but the formula is a standard likelihood-ratio test statistic Q = -2 ln(L1/L2) interpreted as a Gaussian significance; no nuisance parameters are profiled and no CLs modification (which divides by the background-only p-value) is applied. Please align the terminology with the actual statistic.
  2. [§3.3, §4.4] The N-subjettiness ratio τ32 is used extensively but the text does not explicitly define it as τ3/τ2 for each jet; consider adding a sentence in §3.3 after Eq. (3.4) to define the ratio and its role.
  3. [§4.2, §4.3] The event-selection list in §4.3 is clear, but the optimization of the selection thresholds (MET>160 GeV, HT>700 GeV, jet pT>200 GeV) is not discussed; a brief justification or sensitivity check for these choices would improve reproducibility.
  4. [Table 1, §5.2.3] There are a few typographical artifacts: 'T able' in the Table 1 caption and 'V ariables' in the §5.2 heading. Also, in §5.2.3 the BDT classifier for model discrimination is mentioned to use 'only the second set of variables' from Table 5, but the exact feature list (e.g., whether polarization variables are included) is not fully specified; please list the features explicitly.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the analysis is self-contained and its predictions do not reduce to fitted inputs or self-citations.

full rationale

The central claims—signal significances of 4.83 sigma (R2) and 5.39 sigma (S3), and model-discrimination significances up to 3.23 sigma—are obtained from Monte Carlo event generation (MadGraph5 aMC@NLO + Pythia8 + Delphes), externally normalized NLO cross sections, standard significance formulae, and Poisson profile-likelihood comparisons. No equation in the paper reduces to a fitted value; the polarization formulas in Eqs. (5.5) and (5.6) are standard spin-analyzing-power results, and the opposite chiral structures of the S3 and R2 couplings are model inputs, not outputs of the analysis. The b-subjet tagging procedure (Section 3.4) admittedly extrapolates narrow-jet b-tagging efficiencies from Table 2 to subjets inside large-R jets by quoting the paper's statement: 'we did not really perform a detailed analysis of the b-subjet tagging; instead, we have taken a similar approach as implemented in the Delphes b-tagging module.' This is a detector-performance assumption and a source of systematic uncertainty, but it is not a circular step because the tagging efficiency is neither fitted to the signal-region yields nor defined in terms of the final significance; it is an external input applied probabilistically. The paper cites prior work by overlapping authors (Refs. [28], [42], [55]) for the earlier 1-sigma discrimination result, the profile-likelihood procedure, and the dynamic-radius jet algorithm, respectively, but none of these citations supplies a load-bearing premise that forces the present conclusions: the current analysis introduces a new b-subjet tagging method within Delphes, trains separate BDTs for signal-background separation and for S3-versus-R2 classification, and evaluates the discriminator in a signal region selected by an independent BDT score. The model discrimination is not equivalent to the choice of input couplings; it is a genuinely simulated observable-level comparison. The only significant caveat is the unvalidated subjet-tagging assumption, which affects the realism of the exclusion significances but does not constitute circular reasoning.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The analysis relies on standard SM background calculations, a fixed 100% branching ratio to the t nu final state, and fast detector simulation with b-tagging efficiencies taken from CMS narrow-jet measurements. No new particles or forces are invented. The main free choices are jet radii and BDT thresholds, which are optimized to maximize the reported significance and are therefore not model-independent predictions.

free parameters (3)
  • Jet radius R (anti-kt fixed radius) = 1.2 (R2), 0.8 (S3)
    Chosen to maximize signal significance for each model by scanning over R = 0.8, 1.0, 1.2, 1.5 (Section 4.2, Tables 3 and 6-7).
  • Dynamic radius initial R0 = 0.8 (R2), 0.6 (S3)
    Chosen to maximize signal significance for each model by scanning over R0 = 0.5, 0.6, 0.8, 1.0 (Section 4.2, Tables 3 and 6-7).
  • BDT score threshold (signal region) = Optimized per model (not quoted numerically)
    Working point chosen to maximize signal significance (Section 6); this threshold defines the signal region used for the subsequent model-discrimination analysis.
assumptions (4)
  • domain assumption Each leptoquark decays with 100% branching ratio to a top quark and a neutrino (BR(LQ -> t nu) = 1).
    Section 2 assumes the R2/3_2 state decays entirely into t nu, maximizing signal yield. Other decay modes would reduce the signal rate.
  • domain assumption The SM backgrounds listed in Section 4.1 are the only relevant backgrounds; QCD multijet production is not simulated.
    Section 4.1 omits QCD multijet, which could populate the two-large-R-jets plus MET final state through jet mismeasurement. The authors argue dynamic radius helps reject it but do not include it in the background estimate.
  • domain assumption Delphes fast simulation with the b-tagging parametrization of Table 2 adequately models the detector response for subjets.
    All reconstructed-level objects come from Delphes (Section 4.2), and b-tagging efficiencies are taken from CMS narrow-jet measurements and applied to subjets without dedicated validation.
  • domain assumption Top-quark polarization information survives reconstruction, so reconstructed cos theta_b and z_b retain the parton-level chiral asymmetry.
    This is the basis for the model-discrimination analysis in Sections 5 and 6; no closure test is shown demonstrating that reconstruction preserves the polarization asymmetry.

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Cite this review

Pith. "Pith review of Jet Substructure Probe on Scalar Leptoquark Models via Top Polarization." pith.science (2026). https://pith.science/paper/D5Z2LRXI

@misc{pith2026250516328,
  author       = {Pith},
  title        = {Pith review of: Jet Substructure Probe on Scalar Leptoquark Models via Top Polarization},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/D5Z2LRXI}},
  note         = {Machine review of arXiv:2505.16328}
}
abstract

The study of leptoquarks and their couplings to fermions with different chiralities provides a powerful tool for distinguishing among different leptoquark models. As a case study, we focus on two specific third-generation scalar leptoquark models, $S_3$ and $R_2$, which differ in their electroweak quantum numbers and chiral structures of couplings to the top quark, leading to distinct top-quark polarization states. To enhance the efficacy of the analysis, we employ jet substructure techniques like Soft Drop, $N$-subjettiness, and our custom $b$-tagging method, along with other event variables. The analysis has been performed using both fixed radius and dynamic radius jet clustering algorithms. A multivariate analysis using a boosted decision tree (BDT) is performed to isolate signal from the Standard Model background. For a leptoquark mass of 1250 GeV, the analysis achieves a signal significance of up to $5.3\,\sigma$ at the 14 TeV HL-LHC. Furthermore, a $CL_s$-based profile likelihood estimator is applied to polarization-sensitive variables to discriminate between the two models. To enhance separation between the two models, an additional BDT classifier score is obtained by training a BDT network to distinguish between the $S_3$ and $R_2$ models. In the chosen signal region, the BDT classifier score provides a separation score of up to $3.2\,\sigma$, outperforming traditional variables such as $E_b/E_t$ and $\cos\theta_b$.

Figures

Figures reproduced from arXiv: 2505.16328 by the authors.

Figure 1
Figure 1. Leptoquark pair production processes at leading order, coming from Eq. (2.1). In proton-proton collisions, LQs can be pair-produced and singly produced. For scalar LQ, a single coupling between the LQ and quark-lepton pair determines the coupling strength of the Yukawa interaction. On the other hand, vector LQs require two additional coupling constants to account for interactions associated with the electric quadrup… view at source ↗
Figure 2
Figure 2. A schematic diagram of two top-like jets and MET resulting from the pair production of scalar LQs at the LHC is presented. variations. In the NLO, the scale uncertainties are significantly reduced compared to the LO. We use NNPDF30 LO and NNPDF30 NLO PDF sets [100] for event generation at the LO and NLO levels, respectively. The PDF uncertainty in the total cross section is given in brackets, which is smaller than t… view at source ↗
Figure 3
Figure 3. Normalized bin-wise histogram for signal S3 (solid green), R2 (solid blue), and stacked histograms for the backgrounds after applying all the event selection cuts are presented. The distributions are shown for AK12 jets. – 18 – [PITH_FULL_IMAGE:figures/full_fig_p019_3.png] view at source ↗
Figures from the paper (15 more)
Figure 4
Figure 4. Figure 4: The left panel shows the energy fraction distribution of b-tagged subjet in the leading large-R jet, and the right panel presents the distribution of cos θb (J1) for two signal models, S3 (green) and R2 (blue), along with a bin-wise stacked histograms for the backgroun…
Figure 5
Figure 5. Figure 5: Normalized distributions of the final radius of the leading and subleading large-R jets for signal S3 (solid green), R2 (solid blue), and stacked histograms for the backgrounds after applying all the event selection cuts are presented. The distributions are shown for D…
Figure 6
Figure 6. Figure 6: (Left) Correlations between different variables used to train BDT for signal R2 (lower triangle) and background (upper triangle). Variable ranking is shown in the right panel. The variables for large-R jets and their subjets are constructed using dynamic radius anti-kt…
Figure 7
Figure 7. Figure 7: (Left) ROC for the signal (R2) and background separation. The number of signal and background events is displayed on the top and right sides of the plot for a luminosity of 3000 fb−1 . (Right) Signal significance as a function of signal efficiency. The plots are for dy…
Figure 8
Figure 8. Figure 8: Same as [PITH_FULL_IMAGE:figures/full_fig_p026_8.png]
Figure 9
Figure 9. Figure 9: Same as [PITH_FULL_IMAGE:figures/full_fig_p026_9.png]
Figure 10
Figure 10. Figure 10: Distributions of polarization sensitive variables in the signal region (optimized for R2 model with DRAK08 jet algorithm) for R2, S3, B, B +R2, and B +S3, where B represents the SM backgrounds. For the signals, the histogram values are multiplied by a factor of 5 to b…
Figure 11
Figure 11. Figure 11: Same as [PITH_FULL_IMAGE:figures/full_fig_p029_11.png]
Figure 12
Figure 12. Figure 12: Same as [PITH_FULL_IMAGE:figures/full_fig_p034_12.png]
Figure 13
Figure 13. Figure 13: Same as [PITH_FULL_IMAGE:figures/full_fig_p035_13.png]
Figure 14
Figure 14. Figure 14: Same as [PITH_FULL_IMAGE:figures/full_fig_p035_14.png]
Figure 15
Figure 15. Figure 15: Same as [PITH_FULL_IMAGE:figures/full_fig_p036_15.png]
Figure 16
Figure 16. Figure 16: Same as [PITH_FULL_IMAGE:figures/full_fig_p036_16.png]
Figure 17
Figure 17. Figure 17: Same as [PITH_FULL_IMAGE:figures/full_fig_p037_17.png]
Figure 18
Figure 18. Figure 18: Same as [PITH_FULL_IMAGE:figures/full_fig_p037_18.png]

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