[2604.01313] JetPrism: diagnosing convergence for generative simulation and inverse problems in nuclear physics

Computer Science > Machine Learning arXiv:2604.01313 (cs) [Submitted on 1 Apr 2026] Title:JetPrism: diagnosing convergence for generative simulation and inverse problems in nuclear physics Authors:Zeyu Xia, Tyler Kim, Trevor Reed, Judy Fox, Geoffrey Fox, Adam Szczepaniak View a PDF of the paper titled JetPrism: diagnosing convergence for generative simulation and inverse problems in nuclear physics, by Zeyu Xia and 5 other authors View PDF HTML (experimental) Abstract:High-fidelity Monte Carlo simulations and complex inverse problems, such as mapping smeared experimental observations to ground-truth states, are computationally intensive yet essential for robust data analysis. Conditional Flow Matching (CFM) offers a mathematically robust approach to accelerating these tasks, but we demonstrate its standard training loss is fundamentally misleading. In rigorous physics applications, CFM loss plateaus prematurely, serving as an unreliable indicator of true convergence and physical fidelity. To investigate this disconnect, we designed JetPrism, a configurable CFM framework acting as an efficient generative surrogate for evaluating unconditional generation and conditional detector unfolding. Using synthetic stress tests and a Jefferson Lab kinematic dataset ($\gamma p \to \rho^0 p \to \pi^+\pi^- p$) relevant to the forthcoming Electron-Ion Collider (EIC), we establish that physics-informed metrics continue to improve significantly long after the standard loss converges. Conseq...