Document Type

Conference Paper

Publication Date

2024

DOI

10.1088/1748-0221/19/08/C08003

Publication Title

Journal of Instrumentation

Volume

19

Issue

8

Pages

C08003 (1-11)

Conference Name

Artificial Intelligence for the Electron-Ion Collider, 28 November-December 1, 2023, Washington, D. C.

Abstract

We extend the Variational Autoencoder Inverse Mapper (VAIM) framework for the inverse problem of extracting Compton Form Factors (CFFs) from deeply virtual exclusive reactions, such as the unpolarized Deeply virtual exclusive scattering (DVCS) cross section. VAIM is an end-to-end deep learning framework to address the solution ambiguity issue in ill-posed inverse problems, which comprises of a forward mapper and a backward mapper to simulate the forward and inverse processes, respectively. In particular, we incorporate Bayesian Neural Network (BNN) into the VAIM architecture (BNN-VAIM) for uncertainty quantification. By sampling the weights and biases distributions of the BNN in the backward mapper of the VAIM, BNN-VAIM is able to estimate prediction uncertainty associated with each individual solution obtained for an ill-posed inverse problem. We first demonstrate the uncertainty quantification capability of BNN-VAIM in a toy inverse problem. Then, we apply BNN-VAIM to the inverse problem of extracting 8 CFFs from the unpolarized DVCS cross section.

Comments

This is a conference paper that was published in the Journal of Instrumentation, ISSN 1748-0221.

Rights

© 2024 The Authors.

Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0). Any further distribution of this work must maintain attribution to the authors and the title of the work, journal citation and DOI.

Original Publication Citation

Hossen, M. F. B., Alghamdi, T., Almaeen, M., & Li, Y. (2024). Bayesian neural network Variational Autoencoder Inverse Mapper (BNN-VAIM) and its application in Compton Form Factors extraction. 19(8), C08003. https://doi.org/10.1088/1748-0221/19/08/C08003

ORCID

0000-0003-0178-1876 (Li)

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