VAIM-CFF: A variational autoencoder inverse mapper solution to Compton form factor extraction from deeply virtual exclusive reactions

TL;DR

This paper introduces VAIM-CFF, a novel variational autoencoder-based inverse mapping method for extracting Compton form factors from deeply virtual exclusive reactions, enabling access to multiple solutions and underlying physics information.

Contribution

The paper presents a new VAIM framework that improves CFF extraction by capturing multiple solutions and revealing hidden physics information through latent space analysis.

Findings

Access to multiple CFF solutions from a single measurement

Visualization of missing physics information via latent space

Enhanced understanding of inverse problem properties in CFF extraction

Abstract

We develop a new methodology for extracting Compton form factors (CFFs) in from deeply virtual exclusive reactions such as the unpolarized DVCS cross section using a specialized inverse problem solver, a variational autoencoder inverse mapper (VAIM). The VAIM-CFF framework not only allows us access to a fitted solution set possibly containing multiple solutions in the extraction of all 8 CFFs from a single cross section measurement, but also accesses the lost information contained in the forward mapping from CFFs to cross section. We investigate various assumptions and their effects on the predicted CFFs such as cross section organization, number of extracted CFFs, use of uncertainty quantification technique, and inclusion of prior physics information. We then use dimensionality reduction techniques such as principal component analysis to visualize the missing physics information tracked in the latent space of the VAIM framework. Through re-framing the extraction of CFFs as an inverse problem, we gain access to fundamental properties of the problem not comprehensible in standard fitting methodologies: exploring the limits of the information encoded in deeply virtual exclusive experiments.