Scaffolding Simulations with Deep Learning for High-dimensional Deconvolution
Abstract
A common setting for scientific inference is the ability to sample from a highfidelity 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.
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.
Research Areas
