Document Type

Article

Publication Date

2026

DOI

10.1016/j.physletb.2026.140816

Publication Title

Physics Letters B

Volume

880

Pages

140816 (13 pp.)

Abstract

Reconstructing the internal properties of hadrons in terms of fundamental quark and gluon degrees of freedom is a central goal in nuclear and particle physics. This effort lies at the core of major experimental programs, such as the Jefferson Lab 12 GeV program and the upcoming Electron-Ion Collider. A primary challenge is the inherent inverse problem: converting large-scale observational data from collision events into the fundamental quantum correlation functions (QCFs) that characterize the microscopic structure of hadronic systems within the theory of QCD. Recent advances in scientific computing and machine learning have opened new avenues for addressing this challenge using deep learning techniques. A particularly promising direction is the integration of theoretical calculations and experimental simulations into a unified framework capable of reconstructing QCFs directly from event-level information. In this work, we introduce a differential sampling method called the local orthogonal inverse transform sampling (LOITS) algorithm. We validate its performance through a closure test, demonstrating the accurate reconstruction of a test distribution from sampled events using Generative Adversarial Networks. The LOITS algorithm provides a central building block for addressing inverse problems involving QCFs and enables end-to-end inference pipelines within the framework of differential programming.

Rights

© 2026 The Authors.

This is an open access article under the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Data Availability

Article states: "Data will be made available on request."

Original Publication Citation

Braga, K., Diefenthaler, M., Goldenberg, S., Lersch, D., Li, Y., Qiu, J. W., Rajput, K., Ringer, F., Sato, N., & Schram, M. (2026). Toward an event-level analysis of hadron structure using differential programming. Physics Letters B, 880, Article 140816. https://doi.org/10.1016/j.physletb.2026.140816

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