2022/01/28 by M. Boglione, Boglione, M., M. Diefenthaler +16
Physics and Astronomy · #Algorithm #Artificial intelligence #Classical mechanics #Computer science #Deep inelastic scattering #FOS: Physical sciences #Factorization #High Energy Physics - Experiment (hep-ex) #High Energy Physics - Phenomenology (hep-ph) #High Energy Physics - Theory (hep-th) #High-Energy Particle Collisions Research #Inelastic scattering #Kinematics #Nuclear Theory (nucl-th) #Nuclear physics #Optics #Particle Detector Development and Performance #Particle physics #Particle physics theoretical and experimental studies #Physics #Scattering #hep-ex #hep-ph #hep-th #nucl-th
paper · pdf · doi:10.48550/arxiv.2201.12197
31 pages, 17 plots
arxiv created 2022/01/28 · openalex publication_date 2022/01/28 · arxiv updated 2022/01/31 · openalex created_date 2022/07/23 · openalex updated_date 2026/08/06
We introduce a new phenomenological tool based on momentum region indicators to guide the analysis and interpretation of semi-inclusive deep-inelastic scattering measurements. The new tool, referred to as "affinity", is devised to help visualize and quantify the proximity of any experimental kinematic bin to a particular hadron production region, such as that associated with transverse momentum dependent factorization. We apply the affinity estimator to existing HERMES and COMPASS data and expected data from Jefferson Lab and the future Electron-Ion Collider. We also provide an interactive notebook based on Machine Learning for fast evaluation of affinity.