arXiv:2606.16035v1 Announce Type: cross Abstract: Modern particles physics experiments have demonstrated an increasing need for fast, high-fidelity detector simulation as detector components have improved and subsequent computational requirements approach the limits of available resources. Recently, deep generative models have emerged as a promising alternative to traditional Monte-Carlo methods, with recent works drawing inspiration from large language models (LLMs) and self-supervised next-token prediction methods. In this work, we present an application of a GPT-style autoregressive transfo
The increasing computational demands of particle physics simulations, coupled with advancements in deep generative AI models, are driving the exploration of more efficient methods.
This development represents a significant step towards faster, high-fidelity scientific simulations, potentially accelerating discovery in complex fields like particle physics and beyond.
Traditional Monte-Carlo simulation methods for detector hits are being challenged by AI-driven approaches, offering a path to reduce computational bottlenecks.
- · Particle physicists
- · Deep generative AI researchers
- · High-energy physics experiments
- · Traditional Monte-Carlo simulation developers
Reduced computational time and resources for detector simulations in fundamental physics research.
Broader adoption of AI-driven generative models across other computationally intensive scientific domains.
Acceleration of scientific discovery due to more efficient and accessible simulation capabilities.
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