Unbinned Deep Learning Jet Substructure Measurement in High Q2Q^2 ep collisions at HERA

The radiation pattern within high energy quark- and gluon-initiated jets (jet substructure) is used extensively as a precision probe of the strong force as well as an environment for optimizing event generators with numerous applications in high energy particle and nuclear physics. Looking at electron-proton collisions is of particular interest as many of the complications present at hadron colliders are absent. A detailed study of modern jet substructure observables, jet angularities, in electron-proton collisions is presented using data recorded using the H1 detector at HERA. The measurement is unbinned and multi-dimensional, using machine learning to correct for detector effects. All of the available reconstructed object information of the respective jets is interpreted by a graph neural network, achieving superior precision on a selected set of jet angularities. Training these networks was enabled by the use of a large number of GPUs in the Perlmutter supercomputer at Berkeley Lab. The particle jets are reconstructed in the laboratory frame, using the $k_{\mathrm{T}}$ jet clustering algorithm. Results are reported at high transverse momentum transfer $Q^2>150$ GeV${}^2$, and inelasticity $0.2 < y < 0.7$. The analysis is also performed in sub-regions of $Q^2$, thus probing scale dependencies of the substructure variables. The data are compared with a variety of predictions and point towards possible improvements of such models.Comment: 33 pages, 10 figures, 8 table

Can the Routine Use of Patient-Reported Outcome Measures Improve the Delivery of Person-Centered Diabetes Care? A Review of Recent Developments and a Case Study

Purpose of Review: In recent years, the recommendation for and use of patient-reported outcome measures (PROMs) in routine diabetes care has significantly increased. We review recent evidence and highlight key opportunities and challenges related to the active clinical use of PROMs to support person-centered diabetes care and focus areas for future research in the area. Recent Findings: Recent pragmatic studies support that integration of multi-dimensional PROMs for diabetes in clinical care as part of a care improvement strategy can be acceptable for and valued by people with diabetes (PWD) and healthcare professionals (HCPs) and may improve multiple aspects of quality of care, including screening, medical care monitoring and decision support, individualization of self-management support and goal-setting, and broader benefits related to active patient participation and person-centred diabetes care. We identify multiple intervention, individual, and care setting characteristics, which influence acceptability, feasibility, implementation, and effectiveness of PROMs in routine care. Recent clinical PROM studies highlight the value of mixed methods research and systematic involvement of PWD, clinicians, and other stakeholders in the design and implementation of questionnaires for patient input in routine diabetes care. Summary: We identified a new significant trend towards participatory development of multi-dimensional PROMs with the aim of IT-enabled integration into routine diabetes care to facilitate multiple components of person-centered diabetes care and better clinical, quality of life, and cost outcomes. While results from large-scale randomized controlled studies are still limited, a growing number of pragmatic implementation studies support that user-centric PROM interventions have the potential to facilitate significant improvements in care for PWD.</p