Electroweak diboson plus high-mass dijet production observed at 7.4 sigma with signal strength 1.28, plus first semileptonic-channel limits on S02, T0 and M0 Wilson coefficients.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
representative citing papers
An encoder-decoder neural network trained on boundary element method data designs and compares layered cloaks for 2D Helmholtz scattering, showing object-fitted layers reduce scattering more than circular ones for circular, star, and kite objects.
A PINN constrained by the two-component multiplicity model learns the hard-scattering fraction from Zr+Zr events and predicts N_ch more accurately than a data-driven NN on unseen Ru+Ru and Au+Au collisions.
citing papers explorer
-
Electroweak diboson production in association with a high-mass dijet system in semileptonic final states from $pp$ collisions at $\sqrt{s} = 13$ TeV with the ATLAS detector
Electroweak diboson plus high-mass dijet production observed at 7.4 sigma with signal strength 1.28, plus first semileptonic-channel limits on S02, T0 and M0 Wilson coefficients.
-
Solving forward and inverse wave scattering via boundary integral equations and deep learning. Applications to cloaking design
An encoder-decoder neural network trained on boundary element method data designs and compares layered cloaks for 2D Helmholtz scattering, showing object-fitted layers reduce scattering more than circular ones for circular, star, and kite objects.
-
Physics-informed neural network (PINN) modeling of charged particle multiplicity using the two-component framework in heavy-ion collisions: A comparison with data-driven neural networks
A PINN constrained by the two-component multiplicity model learns the hard-scattering fraction from Zr+Zr events and predicts N_ch more accurately than a data-driven NN on unseen Ru+Ru and Au+Au collisions.