Machine learning-based event generator for electron-proton scattering

URN to cite this document:: urn:nbn:de:bvb:355-epub-535945
DOI to cite this document:: 10.5283/epub.53594

Alanazi, Y.

; Ambrozewicz, P.

; Battaglieri, M. ; Hiller Blin, Astrid N. ; Kuchera, M. P.

; Li, Y. ; Liu, T.

; McClellan, R. E. ; Melnitchouk, W.

; Pritchard, E. ; Robertson, M.

; Sato, N. ; Strauss, R. ; Velasco, L.

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Date of publication of this fulltext: 20 Jan 2023 13:37

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Abstract

We present a new machine learning-based Monte Carlo event generator using generative adversarial networks (GANs) that can be trained with calibrated detector simulations to construct a vertex-level event generator free of theoretical assumptions about femtometer scale physics. Our framework includes a GAN-based detector folding as a fast-surrogate model that mimics detector simulators. The ...