• Corpus ID: 51682314

Unfolding with Generative Adversarial Networks

@article{Datta2018UnfoldingWG,
  title={Unfolding with Generative Adversarial Networks},
  author={Kaustuv Datta and Deepak Kar and D. Roy},
  journal={arXiv: Data Analysis, Statistics and Probability},
  year={2018}
}
Correcting measured detector-level distributions to particle-level is essential to make data usable outside the experimental collaborations. The term unfolding is used to describe this procedure. A new method of unfolding the data using a modified Generative Adversarial Network (MSGAN) is presented here. Applied to various distributions, it is demonstrated to perform at par with, or better than, currently used methods. 
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