New tool for kinematic regime estimation in semi-inclusive deep-inelastic scattering

TL;DR

The paper introduces a new phenomenological tool called 'affinity' that helps analyze semi-inclusive deep-inelastic scattering data by visualizing proximity to specific hadron production regions, aiding interpretation and planning for future experiments.

Contribution

It presents a novel 'affinity' estimator based on momentum region indicators, with an interactive machine learning tool for rapid evaluation, applicable to current and future scattering data.

Findings

Successfully applied to HERMES and COMPASS data

Provides a visual and quantitative measure of kinematic proximity

Facilitates analysis for upcoming experiments like EIC

Abstract

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.