{"id":"abb1fc47-ef17-42a6-91f0-e5e8101ab130","arxiv_id":"2605.13237","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":7.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"A resonance-sensitive metric using relative transverse momenta allows cell resampling to reduce negative weights in NLO W+2jets samples while preserving resonance predictions with high accuracy.","lead":"The paper introduces a metric for scattering events based on relative transverse momenta with built-in sensitivity to intermediate resonances. This metric enables cell resampling that substantially cuts negative weights in NLO event samples while keeping resonance properties accurate, demonstrated on W boson plus two jets production.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"Metric's resonance sensitivity and relative-momentum weighting may still allow small systematic shifts in resonance observables that are not bounded by the single W+2jets demonstration.","rationale":"The reader's weakest assumption directly identifies the bias risk arising from the metric's internal choices. Because the full text is now available, the concern can be stated more precisely in terms of the missing parameter scan and cross-check, but the load-bearing point remains the same. This moves the verdict from UNVERDICTED to CONDITIONAL pending the concrete test.","tokens_in":1563,"tokens_out":380,"duration_ms":19789,"concrete_test":"Re-run the cell-resampling algorithm on the same NLO W+2jets sample while varying the resonance-sensitivity weight by a factor of two; recompute the differential distributions in m_{ℓν}, Δφ(ℓ,j), and p_T^W before and after resampling. If any bin deviates by more than 1.5 times the statistical uncertainty relative to the un-resampled sample, the bias claim is not robust.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"The central claim requires that cell resampling with the new metric reduces negative weights while leaving resonance properties (invariant masses, decay angles, etc.) undistorted at the claimed high accuracy. The metric definition incorporates explicit resonance sensitivity and relative transverse momenta; any mismatch between the metric distance and the true likelihood ratio can produce a residual bias when cells are merged or reweighted. The paper demonstrates the procedure on an NLO W+2jets sample, but does not report a systematic scan over the resonance-sensitivity parameter or a comparison against an independent, metric-independent resampling method on the same sample. Without that, it remains possible that the observed preservation is partly due to the specific kinematics of the W resonance rather than a general property of the metric.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The manuscript introduces a metric on the space of scattering events that incorporates relative transverse momenta and explicit sensitivity to intermediate resonances. It claims that cell resampling using this metric can substantially reduce negative weights in Monte Carlo event samples while preserving resonance properties (such as invariant masses and decay angles) to high accuracy. The approach is demonstrated on an NLO sample for leptonically decaying W boson production in association with two jets.","tokens_in":1711,"tokens_out":429,"duration_ms":38963,"significance":"If the central claim holds, the method would provide a valuable, parameter-free tool for improving the statistical efficiency of event generation in high-energy physics, particularly for resonant processes where negative weights degrade sample usability. The explicit grounding in relative momenta and resonance structure, without ad-hoc parameters, is a notable strength that could enhance reproducibility and applicability across similar processes at the LHC.","major_comments":[{"comment":"The demonstration on the NLO W+2jets sample states that resonance properties are preserved with high accuracy but reports no quantitative accuracy metrics, error bounds, or bias assessments (e.g., shifts in invariant mass distributions or decay angles). This is load-bearing for the central claim, as the metric's resonance sensitivity could introduce residual distortions if the distance measure does not align with the true likelihood ratio.","section":"Demonstration"},{"comment":"No systematic variation of the resonance-sensitivity parameter or comparison to a metric-independent resampling baseline is presented on the same sample. Without these, it remains possible that the observed preservation of resonance properties is specific to the W kinematics rather than a general feature of the metric, undermining the claim of unbiased reduction in negative weights.","section":"Metric and Resampling Procedure"}],"minor_comments":[{"comment":"Clarify the explicit functional form of the metric (including how relative transverse momenta and resonance terms are combined) with an equation in the main text rather than relying solely on descriptive text.","section":"Metric Definition"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for their careful reading of the manuscript and for highlighting the potential significance of the resonant-aware metric. We address each major comment below and will revise the manuscript to incorporate additional quantitative support and comparisons where feasible.","responses":[{"response":"We agree that explicit quantitative metrics are necessary to substantiate the claim of high-accuracy preservation. In the revised manuscript we will add direct comparisons of the W-boson invariant-mass and lepton decay-angle distributions before and after resampling. These will include measured shifts, Kolmogorov-Smirnov distances, and bias estimates with statistical error bars derived from the finite sample size, allowing readers to assess any residual distortion relative to the original NLO prediction.","revision_made":"yes","referee_comment":"[Demonstration] The demonstration on the NLO W+2jets sample states that resonance properties are preserved with high accuracy but reports no quantitative accuracy metrics, error bounds, or bias assessments (e.g., shifts in invariant mass distributions or decay angles). This is load-bearing for the central claim, as the metric's resonance sensitivity could introduce residual distortions if the distance measure does not align with the true likelihood ratio."},{"response":"The metric is formulated without a free resonance-sensitivity parameter; resonance awareness enters through the explicit inclusion of intermediate-particle four-momenta in the distance definition. Nevertheless, to address the concern we will add, in the revision, a side-by-side comparison on the identical W+2jets sample between the resonant-aware metric and a baseline metric that uses only relative transverse momenta without resonance information. This will quantify how much the resonance term contributes to the observed preservation of distributions.","revision_made":"partial","referee_comment":"[Metric and Resampling Procedure] No systematic variation of the resonance-sensitivity parameter or comparison to a metric-independent resampling baseline is presented on the same sample. Without these, it remains possible that the observed preservation of resonance properties is specific to the W kinematics rather than a general feature of the metric, undermining the claim of unbiased reduction in negative weights."}],"tokens_in":1195,"tokens_out":443,"duration_ms":47797,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main takeaway is that this paper defines a metric on events that combines differences in relative transverse momenta with explicit weighting toward intermediate resonances, then uses that metric for cell resampling to cut negative weights. On an NLO W+2jets sample the procedure reduces the negative fraction while the resonance mass and decay distributions remain close to the input sample. That combination is the incremental advance over earlier resampling schemes that either used generic distances or handled resonances outside the metric itself. The authors get credit for picking a relevant LHC process and showing the steps are implementable on an existing event generator output. The description is clear enough that someone could code the distance function and repeat the exercise. The soft spot is the narrow evidence base. Everything rests on one process and one choice of resonance-sensitivity parameter, with no reported scan, no error bands on the observable shifts, and no side-by-side run against a resonance-blind version of the same metric. Without those controls it is still possible that the apparent preservation is helped by the kinematics of the W rather than being a general property of the construction. A small systematic tilt in other observables could stay hidden in this demonstration. The paper is aimed at Monte Carlo practitioners who already tune negative-weight samples for resonance processes. A reader who works on NLO generators or reweighting tools will find the metric definition worth testing. The work is coherent and the problem it addresses is real, so it deserves a serious referee rather than a desk rejection. Referees can request the quantitative checks that are missing from the current version.","headline":"New relative-pT plus resonance metric looks workable for negative-weight reduction but the single W+2jets test does not yet bound possible small biases.","tokens_in":2183,"tokens_out":380,"would_cite":false,"duration_ms":20950,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"A metric using relative transverse momenta and resonance sensitivity lets cell resampling cut negative weights while keeping resonance shapes intact.","keywords":["cell resampling","negative weights","NLO event generation","resonance metric","W boson production","jet production","Monte Carlo simulations","relative transverse momentum"],"falsifier":"Generate an independent high-statistics sample or use exact analytic results for the W resonance mass distribution and check whether the resampled sample deviates from it by more than the expected statistical error.","tokens_in":2448,"feed_emoji":"⚛️","tokens_out":592,"duration_ms":28216,"temperature":0.7,"pith_summary":"The paper introduces a distance measure between scattering events that depends on relative transverse momenta and includes explicit terms for intermediate resonances. This measure supports cell resampling of Monte Carlo samples, which removes most negative weights that arise in next-to-leading-order calculations. A reader should care because negative weights make event samples statistically awkward and computationally expensive to handle in collider simulations. The authors test the approach on an NLO sample for a leptonically decaying W boson produced with two jets and report that resonance observables stay accurate after resampling.","feed_headline":"Resonance-aware metric cuts negative weights in NLO samples","feed_subtitle":"Relative momenta and resonance sensitivity let resampling preserve W boson properties in W+2jet events.","key_machinery":"The relative-and-resonant-aware metric that defines event distances from relative transverse momenta and resonance-sensitive terms, enabling targeted cell resampling.","core_discovery":"We present a metric on the space of scattering events based on relative transverse momenta and with explicit sensitivity to intermediate resonances. With this new metric, negative weights in an event sample can be reduced substantially through cell resampling, while preserving the predicted properties of the resonance with high accuracy. We demonstrate the efficiency on a NLO event sample for the production of a leptonically decaying W boson together with two jets.","pith_inferences":["The same metric construction could be tried on other resonance processes such as Z or Higgs production.","If the metric remains stable under higher-order corrections, it may reduce the need for negative-weight handling in future NNLO generators.","Event clustering tools in analysis pipelines might adopt similar relative-momentum distances for background subtraction."],"forward_implications":["Negative weights in NLO samples drop substantially after resampling.","Resonance mass and width distributions remain accurate to high precision.","The method works for processes with clear intermediate resonances such as W plus jets.","Cell resampling becomes usable on larger event samples without spoiling physical predictions."],"fun_headline_variants":["Relative transverse metric improves resonance preservation in resampling","Resonant-sensitive metric trims negatives in W boson event samples","Cell resampling with momentum metric keeps W properties accurate","Metric on scattering events with resonance sensitivity reduces negatives"],"cache_read_input_tokens":64,"weakest_assumption_plain":"The specific choices of relative momenta and resonance weighting in the metric do not introduce hidden biases that change resonance properties or other observables.","fun_headline_variants_meta":{"raw":{"variants":["Relative transverse metric improves resonance preservation in resampling","Resonant-sensitive metric trims negatives in W boson event samples","Cell resampling with momentum metric keeps W properties accurate","Metric on scattering events with resonance sensitivity reduces negatives"]},"model":"grok-4.3","cost_usd":0.007192,"raw_usage":{"total_tokens":3154,"prompt_tokens":501,"num_sources_used":0,"completion_tokens":53,"cost_in_usd_ticks":71915500,"prompt_tokens_details":{"text_tokens":501,"audio_tokens":0,"image_tokens":0,"cached_tokens":64},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":2600,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":501,"tokens_out":53,"duration_ms":22371,"temperature":1.0,"reasoning_tokens":2600,"cache_read_input_tokens":64,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-05-14T18:27:10.832186+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"Generate an independent high-statistics sample or use exact analytic results for the W resonance mass distribution and check whether the resampled sample deviates from it by more than the expected statistical error.","supporting_citations":[],"review_version":1}