2024/11/25 by Annalena Kofler, Kofler, Annalena, Vincent Stimper +7 · 1 voice
Computer Science · Environmental Science · #Computational Physics (physics.comp-ph) #Data Analysis #Data Stream Mining Techniques #FOS: Computer and information sciences #FOS: Physical sciences #High Energy Physics - Phenomenology (hep-ph) #Hydrological Forecasting Using AI #Machine Learning (cs.LG) #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2411.16234
openalex publication_date 2024/11/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
High-energy physics requires the generation of large numbers of simulated data samples from complex but analytically tractable distributions called matrix elements. Surrogate models, such as normalizing flows, are gaining popularity for this task due to their computational efficiency. We adopt an approach based on Flow Annealed importance sampling Bootstrap (FAB) that evaluates the differentiable target density during training and helps avoid the costly generation of training data in advance. We show that FAB reaches higher sampling efficiency with fewer target evaluations in high dimensions in comparison to other methods.