Normalizing Flows for High-Dimensional Detector Simulations

Autor:	Ernst, Florian, Favaro, Luigi, Krause, Claudius, Plehn, Tilman, Shih, David
Rok vydání:	2023
Předmět:	High Energy Physics - Phenomenology
Druh dokumentu:	Working Paper
Popis:	Whenever invertible generative networks are needed for LHC physics, normalizing flows show excellent performance. A challenge is their scaling to high-dimensional phase spaces. We investigate their performance for fast calorimeter shower simulations with increasing phase space dimension. In addition to the standard architecture we also employ a VAE to compress the dimensionality. Our study provides benchmarks for invertible networks applied to the CaloChallenge. Comment: 24 pages, 9 figures, 5 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2312.09290 Zobrazit plný text záznamu View this record from Arxiv