Scaffolding Simulations with Deep Learning for High-dimensional Deconvolution
This addresses the challenge of enabling down-stream inference tasks in fields like particle physics by providing an unbinned and high-dimensional deconvolution method, though it is incremental as it builds on existing iterative Bayesian unfolding techniques.
The paper tackles the problem of high-dimensional deconvolution in scientific inference by proposing OmniFold, a simulation-based maximum likelihood approach that removes detector distortions, accounts for noise processes, and acceptance effects, achieving results that generalize the Richardson-Lucy method.
A common setting for scientific inference is the ability to sample from a high-fidelity forward model (simulation) without having an explicit probability density of the data. We propose a simulation-based maximum likelihood deconvolution approach in this setting called OmniFold. Deep learning enables this approach to be naturally unbinned and (variable-, and) high-dimensional. In contrast to model parameter estimation, the goal of deconvolution is to remove detector distortions in order to enable a variety of down-stream inference tasks. Our approach is the deep learning generalization of the common Richardson-Lucy approach that is also called Iterative Bayesian Unfolding in particle physics. We show how OmniFold can not only remove detector distortions, but it can also account for noise processes and acceptance effects.