Unfolding with Generative Adversarial Networks

Kaustuv Datta; Deepak Kar; Debarati Roy

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arXiv · 1806.00433

Unfolding with Generative Adversarial Networks

Kaustuv Datta·Deepak Kar·Debarati Roy

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Abstract

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 data using a modified Generative Adversarial Network (MSGAN) is presented here. Applied to various distributions with widely different shapes, it performs roughly at par with currently used methods. This is a proof-of-principle demonstration of a state-of-the-art machine learning method that can be used to model detector effects well.

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Kaustuv Datta, Deepak Kar, Debarati Roy. 2018-06-01. Unfolding with Generative Adversarial Networks. https://arxiv.org/abs/1806.00433

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