Deep Generative Models for Detector Signature Simulation: A Taxonomic Review

TL;DR

The paper addresses the high computational cost of detector simulations by surveying deep generative models as fast surrogates. It introduces a unified encoder-sampler-decoder framework and provides a taxonomy across five model classes (VAEs, normalizing flows, GANs, autoregressive models, diffusion) and reviews representative datasets and applications. It analyzes sampling strategies, generation methods, and evaluation approaches across showers, jets, and tracks, arguing for practical use in statistics amplification, amortised generation, OOD/zero-shot generation, and anomaly detection. It further discusses challenges including physics-informed constraints, uncertainty quantification, real-time deployment, and scalability to ultra-high-granularity detectors, outlining avenues for future research.

Abstract

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.

Figures (1)

• Figure 1: Generic architectures of deep Generative models for simulating detector signatures.