Normalizing flows for high-dimensional detector simulations

Journal Article · 05 March 2025 · SciPost Physics

DOI: https://doi.org/10.21468/SciPostPhys.18.3.081 · OSTI ID:2527373

Ernst, Florian; Favaro, Luigi ; Krause, Claudius ; Plehn, Tilman; Shih, David

Whenever invertible generative networks are needed for LHC physics, normalizing flows show excellent performance. In this work, we investigate their performance for fast calorimeter shower simulations with increasing phase space dimension. We use fast and expressive coupling spline transformations applied to the CaloChallenge datasets. In addition to the base flow architecture we also employ a VAE to compress the dimensionality and train a generative network in the latent space. We evaluate our networks on several metrics, including high-level features, classifiers, and generation timing. Our findings demonstrate that invertible neural networks have competitive performance when compared to autoregressive flows, while being substantially faster during generation.

• Sponsoring Organization: USDOE
• OSTI ID: 2527373
• Journal Information: SciPost Physics, Journal Name: SciPost Physics Journal Issue: 3 Vol. 18; ISSN 2542-4653
• Publisher: Stichting SciPostCopyright Statement
• Country of Publication: Netherlands
• Language: English