arXiv:2601.05289v2 Announce Type: replace-cross Abstract: The high-dimensional complex nature of detectors makes fast calorimeter simulations a prime application for modern generative machine learning. Vision transformers (ViTs) can emulate the Geant4 response with unmatched accuracy and are not limited to regular geometries. Starting from the CaloDREAM architecture, we demonstrate the robustness and scalability of ViTs on regular and irregular geometries, and multiple detectors. Our results show that ViTs generate electromagnetic and hadronic showers with minimal deviations from Geant4 in mul
Advances in generative AI, particularly Vision Transformers, have reached a maturity where they can accurately emulate complex physical simulations, providing an immediate opportunity for breakthroughs in scientific research and engineering.
This development significantly accelerates high-dimensional scientific simulations, especially in fields like high-energy physics, reducing computational costs and opening new avenues for discovery that were previously computationally prohibitive.
The reliance on traditional, computationally expensive simulation methods (like Geant4) lessens, replaced by faster, AI-driven alternatives that maintain high accuracy even with complex and irregular geometries.
- · High-energy physics research institutions
- · AI compute providers
- · Particle accelerator facilities
- · Generative AI model developers
- · Traditional simulation software vendors (if they do not adapt)
- · Researchers without access to advanced AI infrastructure
Faster and cheaper experimental design and analysis in particle physics.
Expansion of AI-driven simulation techniques to other complex engineering and scientific domains beyond high-energy physics.
Potential for new types of experiments and discoveries previously unfeasible due to computational limitations, leading to breakthroughs in fundamental science.
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