CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters
arXiv:2606.04165v1 Announce Type: cross Abstract: High-precision calorimeter simulation at current and future colliders imposes rapidly growing computational demands, motivating the development of machine-learning surrogates for traditional Monte Carlo tools such as Geant4. Flow matching and diffusion-based generative models have become leading approaches for high-dimensional fast simulation because of their sample quality, but typically require ${\cal O}(100)$ function evaluations at inference and often rely on auxiliary networks to constrain global observables, compromising streamlined end-t
The increasing computational demands of high-precision simulations for current and future colliders necessitate more efficient methods than traditional Monte Carlo tools.
This research represents a significant step towards developing faster, more accurate machine learning surrogates for complex scientific simulations, impacting fundamental physics research and potentially other high-fidelity simulation needs.
The reliance on computationally intensive traditional simulation methods can decrease, opening possibilities for more rapid scientific discovery and analysis in high-energy physics.
- · High-energy physics research
- · Particle accelerator labs
- · AI/ML researchers
- · Scientific computing
- · Traditional Monte Carlo simulation tools
- · Computational resource-intensive experimental design
Scientific discovery in high-energy physics accelerates due to faster and more efficient simulation capabilities.
The methodologies developed here could be adapted to other scientific domains requiring high-fidelity simulations, such as materials science or climate modeling.
Reduced computational costs for fundamental research could lower barriers to entry for smaller research institutions, democratizing advanced scientific inquiry.
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Read at arXiv cs.LG