CALPAGAN: Calorimetry for Particles Using Generative Adversarial Networks

Simsek E., IŞILDAK B., Dogru A., Aydogan R., Bayrak B., ERTEKİN BOLELLİ Ş.

Progress of Theoretical and Experimental Physics, cilt.2024, sa.8, 2024 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 2024 Sayı: 8
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1093/ptep/ptae106
  • Dergi Adı: Progress of Theoretical and Experimental Physics
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Applied Science & Technology Source, INSPEC, zbMATH, Directory of Open Access Journals
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

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.