GAN-based Event-level Inverse Mapper (GEIM) - An Application on Quantum Chromodynamics Global Analysis
College
College of Sciences
Department
Computer Science
Graduate Level
Doctoral
Presentation Type
Poster Presentation
Abstract
The inverse problem, aiming at determining the unknown cause given an observed effect, is a fundamental challenge in scientific investigations. In the field of high-energy physics, understanding the complexities of quantum chromodynamics (QCD) relies on analyzing multi-dimensional quantum correlation functions (QCFs), which are derived from experimentally observed events. While the mapping from parameters to observable events in QCFs is a well-posed problem with unique solutions, similar to a general inverse problem of deriving parameters from observables, the inverse problem of inferring parameters from observed events, poses unique challenges due to its ill-posedness. This paper introduces a machine learning-based framework based on generative adversarial networks (GANs), the so-called GAN-based Event-level Inverse Mapper (GEIM), which is designed to address the inverse problem of femtoscale imaging in QCD. GEIM consists of two GANs: the conditional GAN-based \textit {surrogate event generator}, which replaces the physics-based QCF model to generate synthetic events, and the \textit {outer-GAN}, which performs the backward mapping to derive the parameter distributions. Through a proxy 1D QCF analysis, we demonstrate the efficacy of GEIM in accurately learning the mapping between observable events and QCF parameter spaces, deriving QCF parameters from event-level analysis, and eventually reconstructing QCFs.
Keywords
Inverse problem, QCF analysis, GAN, Quantum Chromodynamics.
GAN-based Event-level Inverse Mapper (GEIM) - An Application on Quantum Chromodynamics Global Analysis
The inverse problem, aiming at determining the unknown cause given an observed effect, is a fundamental challenge in scientific investigations. In the field of high-energy physics, understanding the complexities of quantum chromodynamics (QCD) relies on analyzing multi-dimensional quantum correlation functions (QCFs), which are derived from experimentally observed events. While the mapping from parameters to observable events in QCFs is a well-posed problem with unique solutions, similar to a general inverse problem of deriving parameters from observables, the inverse problem of inferring parameters from observed events, poses unique challenges due to its ill-posedness. This paper introduces a machine learning-based framework based on generative adversarial networks (GANs), the so-called GAN-based Event-level Inverse Mapper (GEIM), which is designed to address the inverse problem of femtoscale imaging in QCD. GEIM consists of two GANs: the conditional GAN-based \textit {surrogate event generator}, which replaces the physics-based QCF model to generate synthetic events, and the \textit {outer-GAN}, which performs the backward mapping to derive the parameter distributions. Through a proxy 1D QCF analysis, we demonstrate the efficacy of GEIM in accurately learning the mapping between observable events and QCF parameter spaces, deriving QCF parameters from event-level analysis, and eventually reconstructing QCFs.