  In modern collider experiments, the quest to explore fundamental interactions
between elementary particles has reached unparalleled levels of precision.
Signatures from particle physics detectors are low-level objects (such as
energy depositions or tracks) encoding the physics of collisions (the final
state particles of hard scattering interactions). The complete simulation of
them in a detector is a computational and storage-intensive task. To address
this computational bottleneck in particle physics, alternative approaches have
been developed, introducing additional assumptions and trade off accuracy for
speed.The field has seen a surge in interest in surrogate modeling the detector
simulation, fueled by the advancements in deep generative models. These models
aim to generate responses that are statistically identical to the observed
data. In this paper, we conduct a comprehensive and exhaustive taxonomic review
of the existing literature on the simulation of detector signatures from both
methodological and application-wise perspectives. Initially, we formulate the
problem of detector signature simulation and discuss its different variations
that can be unified. Next, we classify the state-of-the-art methods into five
distinct categories based on their underlying model architectures, summarizing
their respective generation strategies. Finally, we shed light on the
challenges and opportunities that lie ahead in detector signature simulation,
setting the stage for future research and development.
