Flow Annealed Importance Sampling Bootstrap meets Differentiable Particle Physics

Autor:	Kofler, Annalena, Stimper, Vincent, Mikhasenko, Mikhail, Kagan, Michael, Heinrich, Lukas
Rok vydání:	2024
Předmět:	High Energy Physics - Phenomenology Computer Science - Machine Learning Physics - Computational Physics Physics - Data Analysis Statistics and Probability
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted at the 'Machine Learning and the Physical Sciences 2024' workshop at NeurIPS
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2411.16234 Zobrazit plný text záznamu View this record from Arxiv