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2022

Solving Simulation Systematics in and with AI/ML

Viren, Brett, Huang, Jin, Huang, Yi et al.

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Training an AI/ML system on simulated data while using that system to infer on data from real detectors introduces a systematic error which is difficult to estimate and in many analyses is simply not confronted.

It is crucial to minimize and to quantitatively estimate the uncertainties in such analysis and do so with a precision and accuracy that matches those that AI/ML techniques bring.
Here we highlight the need to confront this class of systematic error, discuss conventional ways to estimate it and describe ways to quantify and to minimize the uncertainty using methods which are themselves based on the power of AI/ML.
We also describe methods to introduce a simulation into an AI/ML network to allow for training of its semantically meaningful parameters.

Solving Simulation Systematics in and with AI/ML · Around

Built on

“GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium”
Martin Heusel et al · 2017
Earlier work this paper cites.
“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”
Jun-Yan Zhu, Taesung Park, Phillip Isola and Alexei Efros · 2017
Earlier work this paper cites.
“Ionization Electron Signal Processing in Single Phase LAr TPCs I: Algorithm Description and Quantitative Evaluation with MicroBooNE Simulation”
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MicroBooNE collaboration · 2018
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