Transverse-momentum resummation for boson plus jet production at hadron colliders

We consider the associated production of a vector or Higgs boson with a jet in hadronic collisions. When the transverse momentum q T of the boson-jet system is much smaller than its invariant mass Q, the QCD perturbative expansion is affected by large logarithmic terms that must be resummed to all orders. We discuss the all-order resummation structure of the logarithmically enhanced contributions up to next-to-leading logarithmic accuracy. Resummation is performed at the differential level with respect to the kinematical variables of the boson-jet system. Soft-parton radiation produces azimuthal correlations that are fully accounted for in our framework. We present explicit analytical results for the resummation coefficients up to next-to-leading order and next-to-leading logarithmic accuracy, that include the exact dependence on the jet radius.

Jet Charge: A Flavor Prism for Spin Asymmetries at the Electron-Ion Collider

We propose the jet charge observable as a novel probe of flavor structure in the nucleon spin program at the electron ion collider (EIC) and develop the underlying framework from first principles. We show that jet charge measurements can substantially enhance the sensitivity of spin asymmetries to different partonic flavors in the nucleon. This sensitivity can be further improved by constructing the jet charge using only a subset of hadron species (pions or kaons) in the jet. As an example, we use the Sivers asymmetry in back-to-back electron-jet production at the EIC to show that the jet charge can be a unique tool in constraining the Sivers function for different partonic flavors.

Transverse Momentum Resummation for Z boson Plus Jet Production at the LHC

We study the transverse momentum resummation effects for the Z boson plus jet associated production at the LHC within the transverse momentum dependent (TMD) factorization formalism. The large logarithm ln (Q2/q⊥2) with Q ≫ q⊥ has been resumed to Next-to-Leading Logarithm (NLL) accuracy. We compare our numerical results to the CMS data by reweighting method. It shows that our prediction agree with the data very well.