Toward an event-level analysis of hadron structure using differential programming

TL;DR

This paper introduces the LOITS algorithm, a differential sampling method that reconstructs quantum correlation functions from collision event data, advancing the analysis of hadron structure in nuclear physics.

Contribution

The paper presents a novel LOITS algorithm that integrates differential programming with deep learning to reconstruct QCFs directly from event-level data.

Findings

LOITS accurately reconstructs test distributions from sampled events.

Validation with GANs demonstrates the method's effectiveness.

Enables end-to-end inference pipelines for hadron structure analysis.

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.