Deep Generative Models for Detector Signature Simulation: An Analytical
  Taxonomy
Hosein Hashemi Claudius Krause
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 encoding the
physics of collisions. The complete simulation of them in a detector is a
memory and storage-intensive task. To address this computational bottleneck in
particle physics, "Fast Simulation" has been introduced and refined over the
years. 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 four distinct categories based on their
underlying model architectures, summarizing their respective generation
strategies. We then identify and discuss three key application areas. Finally,
we shed light on the challenges and opportunities that lie ahead in detector
signature simulation, setting the stage for future research and development.