https://doi.org/10.1140/epjc/s10052-025-14091-3
Regular Article - Theoretical Physics
VAIM-CFF: a variational autoencoder inverse mapper solution to Compton form factor extraction from deeply virtual exclusive reactions
1
Department of Software Engineering, College of Computer and Information Sciences, Jouf University, 72341, Sakaka, Aljouf, Saudi Arabia
2
Department of Computer Science, Old Dominion University, 23529, Norfolk, VA, USA
3
Department of Computer Science, College of Computer Science and Information Technology, Al-Baha University, 65779, Al-Baha, Alaqiq, Saudi Arabia
4
High Energy Physics Division, Argonne National Laboratory, 60439, Lemont, IL, USA
5
Department of Physics, University of Virginia, 22904, Charlottesville, VA, USA
6
Department of Physics, Michigan State University, 48824, East Lansing, MI, USA
Received:
9
December
2024
Accepted:
15
March
2025
Published online:
8
May
2025
We develop a new methodology for extracting Compton form factors (CFFs) 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.
© The Author(s) 2025
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