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Constraining the Higgs Potential with Neural Simulation-based Inference for Di-Higgs Production
Determining the form of the Higgs potential is one of the most exciting challenges of modern particle physics.
Flows for Flows: Morphing one Dataset into another with Maximum Likelihood Estimation
We propose a protocol called flows for flows for training normalizing flows to morph one dataset into another even if the underlying probability density of neither dataset is known explicitly.
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