DIS2023: XXX International Workshop on Deep-Inelastic Scattering and Related Subjects

Machine learning-assisted measurement of multi-differential lepton-jet correlations in deep-inelastic scattering with the H1 detector

29 Mar 2023, 10:50

20m

104AB (MSU Kellogg Center)

Parallel talk WG4: QCD with Heavy Flavours and Hadronic Final States WG4

Speaker

Yao Xu (Berkeley University)

Description

The lepton-jet momentum imbalance in deep inelastic scattering events offers a useful set of observables for unifying collinear and transverse-momentum-dependent frameworks for describing high energy Quantum Chromodynamics (QCD) interactions. The imbalance in the laboratory frame was measured recently [1] using positron-proton collisions from HERA Run II. With a new machine learning method, the measurement was performed simultaneously and unbinned in eight dimensions, however the results were projected onto four key observables. This paper extends over those results by showing the multi-differential nature of the unfolded result. In particular, the lepton-jet correlation observables are measured differentially in kinematic properties of the scattering process, the momentum transfer $Q^2>150$ GeV$^2$ and inelasticity $0.210$ GeV and pseudorapidity $-1<\eta<2.5$. The results are compared with parton shower Monte Carlo predictions as well as with calculations from perturbative QCD and from a Transverse Momentum Dependent (TMD) factorization framework. The measurement in bins of $Q^2$ probes the scale evolution of the azimuthal decorrelation.

[1] PRL 128 (2022) 123003

Presentation materials

H1 Lepton-jet correlation unfolding (Talk final slides).pdf