Inference of cosmic-ray source properties by conditional invertible neural networks

@article{Bister2021InferenceOC,
  title={Inference of cosmic-ray source properties by conditional invertible neural networks},
  author={Teresa Bister and Martin Erdmann and U. K{\"o}the and Josina Schulte},
  journal={The European Physical Journal C},
  year={2021},
  volume={82}
}
The inference of physical parameters from measured distributions constitutes a core task in physics data analyses. Among recent deep learning methods, so-called conditional invertible neural networks provide an elegant approach owing to their probability-preserving bijective mapping properties. They enable training the parameter-observation correspondence in one mapping direction and evaluating the parameter posterior distributions in the reverse direction. Here, we study the inference of… 

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