ScatterPrism: convergence for generative simulation and inverse problems in particle and nuclear physics
arXiv:2604.01313v2 Announce Type: replace Abstract: High-fidelity simulations and complex inverse problems, such as detector modeling and unfolding, are computationally intensive bottlenecks across subatomic physics, yet essential for accurate physical interpretation. While Conditional Flow Matching (CFM) offers a robust acceleration approach, we demonstrate its standard training loss is fundamentally misleading. Specifically, utilizing a Jefferson Lab Nuclear Physics (NP) kinematic dataset ($\gamma p \to \rho^0 p \to \pi^+\pi^- p$), we expose that CFM loss plateaus prematurely, obscuring ongo
The continuous drive for higher fidelity and efficiency in scientific simulations, particularly in subatomic physics, necessitates ongoing algorithmic improvements in generative models like Conditional Flow Matching.
Improving the accuracy and reliability of AI models for complex scientific simulations can significantly accelerate discovery, reduce computational bottlenecks, and enhance the interpretation of experimental data in critical research fields.
The understanding of training loss efficacy in Conditional Flow Matching for scientific applications is refined, leading to more robust and accurate generative simulations and inverse problem solutions.
- · AI/ML researchers in scientific computing
- · Particle and nuclear physics research institutions
- · Generative AI model developers
- · Research groups relying on standard CFM implementations without critical evaluat
- · Computational approaches that offer lower fidelity or higher resource intensity
More accurate and faster simulations will lead to quicker scientific advancements in high-energy physics.
The refined understanding of AI training dynamics could generalize to improving other scientific and engineering AI applications.
Reduced computational costs and accelerated discovery might free up resources or enable entirely new experimental designs previously deemed too complex.
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Read at arXiv cs.LG