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

3 May 2022, 10:40
20m
Parallel talk WG5: Spin and 3D Structure WG5: Spin and 3D Structure

Speaker

Miguel Arratia (University of California at Riverside)

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 interactions. A recent first measurement was made [1] of this imbalance in the laboratory frame using positron-proton collision data recordedf with the H1 experiment at HERA in the years 2006-2007. Using a new machine learning method, the measurement was performed simultaneously and unbinned in eight dimensions. The first results were presented as as set of four one-dimensional projections onto key observables. This work extends over those results by making use of the multi-differential nature of the unfolded result. In particular, distributions of lepton-jet correlation observables are studied as a function of the kinematic properties of the scattering process, i.e. as a function of the momentum transfer $Q^2>150$ GeV$^2$ and the inelasticity $0.2< y< 0.7$.

H1prelim-22-031
[1] arxiv:2108.12376, submitted to PRL

Submitted on behalf of a Collaboration? Yes

Authors

Collaboration H1 (DESY) Stefan Schmitt (Deutsches Elektronen-Synchrotron (DE)) Miguel Arratia (University of California at Riverside)

Presentation materials