Scaffolding Simulations with Deep Learning for High-dimensional Deconvolution

Abstract

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.

arXiv comment6 pages, 1 figure, 1 table

References in corpus (5)

• Adam: A Method for Stochastic Optimization
• OmniFold: A Method to Simultaneously Unfold All Observables
• Unfolding with Generative Adversarial Networks
• A Neural Resampler for Monte Carlo Reweighting with Preserved Uncertainties
• Machine learning approach to inverse problem and unfolding procedure

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• Generative Unfolding with Distribution Mapping
• Measurement of lepton-jet correlation in deep-inelastic scattering with the H1 detector using machine learning for unfolding
• How to Unfold Top Decays
• Tools for Unbinned Unfolding