No excess observed in search for vector-like top quark T to tH in opposite-sign dilepton final states; 95% CL upper limits on sigma times BR range from 2.0 pb at 600 GeV to 0.1 pb at 1000 GeV.
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Parton distributions from high-precision collider data
Tool reference. 71% of classified Pith citations use this work as a method, library, or software dependency, not as a substantive claim.
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abstract
We present a new set of parton distributions, NNPDF3.1, which updates NNPDF3.0, the first global set of PDFs determined using a methodology validated by a closure test. The update is motivated by recent progress in methodology and available data, and involves both. On the methodological side, we now parametrize and determine the charm PDF alongside the light quarks and gluon ones, thereby increasing from seven to eight the number of independent PDFs. On the data side, we now include the D0 electron and muon W asymmetries from the final Tevatron dataset, the complete LHCb measurements of W and Z production in the forward region at 7 and 8 TeV, and new ATLAS and CMS measurements of inclusive jet and electroweak boson production. We also include for the first time top-quark pair differential distributions and the transverse momentum of the Z bosons from ATLAS and CMS. We investigate the impact of parametrizing charm and provide evidence that the accuracy and stability of the PDFs are thereby improved. We study the impact of the new data by producing a variety of determinations based on reduced datasets. We find that both improvements have a significant impact on the PDFs, with some substantial reductions in uncertainties, but with the new PDFs generally in agreement with the previous set at the one sigma level. The most significant changes are seen in the light-quark flavor separation, and in increased precision in the determination of the gluon. We explore the implications of NNPDF3.1 for LHC phenomenology at Run II, compare with recent LHC measurements at 13 TeV, provide updated predictions for Higgs production cross-sections and discuss the strangeness and charm content of the proton in light of our improved dataset and methodology. The NNPDF3.1 PDFs are delivered for the first time both as Hessian sets, and as optimized Monte Carlo sets with a compressed number of replicas.
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representative citing papers
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A framework based on linear response and influence functions maps data sensitivities in global QCD analyses to show how experiments determine central values, uncertainties, and correlations of non-perturbative functions.
Quantum tomography of diboson spin density matrices yields superior sensitivity to CP-even and CP-odd SMEFT contributions, including quadratic new-physics terms not captured by traditional angular observables.
Physics-informed neural networks extract a model-independent color dipole amplitude from inclusive HERA data that predicts exclusive J/ψ photoproduction cross-sections without parameter retuning.
First observation of electroweak photon plus two jets production yields a cross section of 202 fb consistent with the standard model prediction of 177 fb at greater than 5 sigma significance.
No excess observed; first LHC search sets 95% CL upper limits on H to AA to 4e branching fraction down to 10^{-5} for 10-100 MeV masses and short lifetimes.
Differential W to muon neutrino cross-section measurement at 5.02 TeV with proof-of-principle W mass extraction from corrected distributions.
Recasting ttZ analyses yields sub-permille constraints on Br(t→H±b)×Br(H±→WZ) and vΔ≲2 GeV in the Y=0 Higgs triplet model, accompanied by a 2σ preference for a non-zero signal.
Higher-order Fisher tensors in exponential-family coordinates of binned energy correlators are simultaneously local KL coefficients, connected cumulants, and hyperedge weights, enabling hypergraph constructions for jet substructure analysis.
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No significant excess observed in four-lepton events from heavy resonances, setting upper limits on production cross sections for dilepton masses 0.4-15 GeV using novel merged-object techniques.
CMS develops two multivariate models for identifying boosted dielectrons with γ_L > 20, reporting 80% efficiency for two-track cases from J/ψ data and 60% for single-track from Z conversions, plus an energy correction.
A light-front spectator model yields the first calculation of Im(F^g_{1,4}) and the resulting sin(2φ) asymmetry in ep → epπ⁰ at EIC kinematics.
No excess found in search for T' to top plus phi; excludes T' masses 0.85-1.3 TeV at 95% CL for SM Higgs case with 5% width, and sets cross-section limits as low as 0.1 fb.
PaRT achieves >50% tagging efficiency for boosted H->WW jets at 1% background efficiency, decorrelated from jet mass, with data-to-simulation scale factors of 0.9-1.0 on 138 fb^{-1} of 13 TeV collisions.
No significant excess is observed in leptonic W/Z plus high-multiplicity soft-particle events, setting limits on Higgs to SUEP decays across a range of model parameters.
First evidence for non-zero φ_s in B_s^0 → J/ψ φ decays at 3.2σ from CMS 13 TeV data combined with prior 8 TeV result.
The work supplies a modular SMEFT likelihood for EWPD that includes NLO effects and five input schemes, then combines it with LHC measurements to tighten constraints on lepton-flavor universality and quark coupling asymmetries.
No significant excess observed; 95% CL exclusion of higgsino electroweakinos with mass splittings 0.28-1.15 GeV and chargino masses up to 185 GeV using soft-track and neural-network selection.
CMS excludes κ_VV outside 0.40-1.60 at 95% CL and constrains κ_2W and κ_2Z using VBS events with a boosted Higgs to bb decay.
No evidence found; excludes semiclassical black holes below 8.4-11.4 TeV and string balls below 9.0-10.7 TeV at 95% CL, and caps the quark-quark sphaleron fraction above 9 TeV at 0.0034.
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Measurement of the $W \to \mu \nu_\mu$ cross-sections as a function of the muon transverse momentum in $pp$ collisions at 5.02 TeV
Differential W to muon neutrino cross-section measurement at 5.02 TeV with proof-of-principle W mass extraction from corrected distributions.
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DeepMET: Improving missing transverse momentum estimation with a deep neural network
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