CALPAGAN: Calorimetry for Particles Using Generative Adversarial Networks

Date

2024-08-01

Author

Simsek, Ebru
IŞILDAK, Bora
Dogru, Anil
Aydogan, Reyhan
Bayrak, Burak
Ertekin Bolelli, Şeyda

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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In this study, a novel approach is demonstrated for converting calorimeter images from fast simulations to those akin to comprehensive full simulations, utilizing conditional Generative Adversarial Networks (GANs). The concept of Pix2pix is tailored for CALPAGAN, where images from fast simulations serve as the basis (condition) for generating outputs that closely resemble those from detailed simulations. The findings indicate a strong correlation between the generated images and those from full simulations, especially in terms of key observables like jet transverse momentum distribution, jet mass, jet subjettiness, and jet girth. Additionally, the paper explores the efficacy of this method and its intrinsic limitations. This research marks a significant step towards exploring more efficient simulation methodologies in high-energy particle physics.

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https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85201099280&origin=inward
https://hdl.handle.net/11511/110688

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Department of Computer Engineering, Article