  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.
