A factorization framework and two-qubit description allow photon pairs at lepton colliders to be treated as qubits for measuring Bell inequality violation, quantum discord, and nonstabilizerness using Belle data.
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MadGraph 5 : Going Beyond
Mixed citation behavior. Most common role is background (59%).
29
Pith papers citing it
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59% of classified citations
abstract
MadGraph 5 is the new version of the MadGraph matrix element generator, written in the Python programming language. It implements a number of new, efficient algorithms that provide improved performance and functionality in all aspects of the program. It features a new user interface, several new output formats including C++ process libraries for Pythia 8, and full compatibility with FeynRules for new physics models implementation, allowing for event generation for any model that can be written in the form of a Lagrangian. MadGraph 5 builds on the same philosophy as the previous versions, and its design allows it to be used as a collaborative platform where theoretical, phenomenological and simulation projects can be developed and then distributed to the high-energy community. We describe the ideas and the most important developments of the code and illustrate its capabilities through a few simple phenomenological examples.
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representative citing papers
No significant excess found in low-mass dijet spectrum from pile-up collisions; exclusion limits set on Gaussian and simplified dark matter models with 1.3 pb^{-1} effective luminosity.
Boosted light scalars decaying to b b-bar in Type-I 2HDM can be tagged as double-b fat-jets and used with SM gauge bosons to probe heavy scalars up to 540 GeV at the HL-LHC for masses 30-70 GeV.
Non-supersymmetric spin-3/2 dark matter with baryon-violating portals can explain the relic abundance through UV and Boltzmann-suppressed freeze-in, with viable parameter space constrained by indirect detection, direct detection, and LHC monojet searches.
Template-Adapted Mixture Model uses many biased simulations for data-driven estimates of signal and background distributions, yielding unbiased signal fraction estimates with well-calibrated uncertainties.
MadGraph5_aMC@NLO automates tree-level, NLO, shower-matched, and merged cross-section computations for collider processes in a unified flexible framework.
Spectral functions from two-point correlations serve as multiplicity-independent ML inputs and improve expected gluino mass reach by 150-250 GeV in a fully hadronic ttbar vs gluino benchmark.
A UV-complete neutron portal model dynamically solves the dark matter-baryon coincidence via a supercooled dark confinement transition that generates GeV-scale asymmetric DM and links to observed gravitational waves.
ALPs with LFV couplings above the muon mass threshold can be produced in LFV meson, tau, and gauge boson decays, yielding clean eμ signatures that enable new searches at future experiments.
Phenomenological LHC study finds sensitivity to light spin-1 DM mediators in ttbar events and discrimination power from CP-sensitive angular observables in dileptonic final states.
Muon collider sensitivity to dark Z' via radiative return versus vector boson fusion allows mixing parameter extraction from relative rates.
A hyper-graph neural network improves discrimination of four-top production at 13 TeV, raising expected significance from 5.13 to 9.11 and enabling projected 95% CL limits on five dimension-six SMEFT Wilson coefficients at current and HL-LHC luminosities.
A framework based on the YFS theorem enables process-independent local IR subtraction and resummation matching for automated NNLO_EW calculations in lepton collider processes.
Proposes construction of the Forward Physics Facility at the HL-LHC with four complementary detectors to exploit forward neutrinos and new-particle fluxes for neutrino, QCD, astroparticle, and dark-matter measurements.
Projections show the μTRISTAN same-sign muon collider can significantly improve bounds on anomalous quartic gauge couplings via vector boson scattering in multiple final states at 2 and 6 TeV.
In a vector dark matter extension of the Higgs portal, far detectors at colliders can probe otherwise inaccessible parameter space and set novel bounds on the reheating temperature.
Future muon colliders can probe Kaluza-Klein excitations of a 5D U(1)_{Lμ-Lτ} gauge boson across MeV to TeV masses with couplings down to 10^{-5}.
DELPHES 3 delivers a modular fast-simulation framework with particle-flow and pile-up features for reconstructing physics objects in collider detector studies.
Collider experiments can strongly constrain p-wave-suppressed derivative operators and thereby limit reheating temperature, DM mass, and interaction scale needed to match observed DM abundance during reheating.
FCC-ee can probe ALP-photon couplings down to a few 10^{-6} GeV^{-1} at the Z pole and ~10^{-5} GeV^{-1} at higher energies for ALP masses 5-320 GeV in the three-photon channel.
No evidence found for additional Higgs boson pairs in Type-X 2HDM; new limits exclude the model for explaining the muon anomalous magnetic moment anomaly.
CMS observes electroweak W±W± and WZ production with two jets at 13.6 TeV with >5σ significance in all-leptonic final states using 171 fb⁻¹ of data.
Projected constraints on four-fermion operators for μ e → t q at 346 GeV improve current LHC bounds by roughly an order of magnitude at 100 fb⁻¹ and more at 1 ab⁻¹.
ATLAS data on eμ invariant mass and angle distributions in 13 TeV collisions are better described by ttbar models that include color-singlet quasi-bound states near threshold, with fits showing over 3 sigma evidence and a cross-section matching dedicated threshold studies.
citing papers explorer
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Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in Physics
The paper defines interpretability as model structural transparency and explainability as scientific content mapping, discusses their trade-offs, and frames both as deliberate modeling choices for ML in physics.