Unifying Simulation and Inference with Normalizing Flows

• URL: http://arxiv.org/abs/2404.18992v2
• Date: Thu, 9 May 2024 21:41:49 GMT
• Title: Unifying Simulation and Inference with Normalizing Flows
• Authors: Haoxing Du, Claudius Krause, Vinicius Mikuni, Benjamin Nachman, Ian Pang, David Shih,
• Abstract summary: We show that two tasks can be unified by using maximum likelihood estimation (MLE) from conditional generative models for energy regression. Using an ATLAS-like calorimeter simulation, we demonstrate this concept in the context of calorimeter energy calibration.
• Score: 0.08796261172196743
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: There have been many applications of deep neural networks to detector calibrations and a growing number of studies that propose deep generative models as automated fast detector simulators. We show that these two tasks can be unified by using maximum likelihood estimation (MLE) from conditional generative models for energy regression. Unlike direct regression techniques, the MLE approach is prior-independent and non-Gaussian resolutions can be determined from the shape of the likelihood near the maximum. Using an ATLAS-like calorimeter simulation, we demonstrate this concept in the context of calorimeter energy calibration.

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