Speaker
Description
Detector simulation is among the most resource-intensive components of modern collider experiments, currently consuming roughly half of the LHC computing budget and more still in the High-Luminosity phase. Normalizing flows are attractive surrogate models for fast simulation: they sit on the Pareto frontier between inference speed and accuracy compared with competing generators such as VAEs and GANs, and support both generation and tractable density estimation. Nevertheless, closing the measurable fidelity gap—especially at high detector granularity—is necessary for flows to be truly competitive. In this study, we improve the generation quality of normalizing flows without modifying their architecture.
First, we show that a classifier trained to distinguish reference from synthetic showers provides an effective estimator of the density ratio between the two distributions. Using this ratio with acceptance–rejection and Markov chain Monte Carlo (MCMC) sampling, we achieve better agreement with the reference distribution across a range of physically motivated high-level observables. This generic technique can be used in principle to improve any generative model.
Second, exploiting the bijective nature of flows, we perform the same resampling directly in latent space, where it is much cheaper than in data space. A classifier-based density-ratio estimate between the latent and base distributions is used to resample the base distribution before the flow's backward transformation, achieving comparable fidelity gains with minimal additional time and compute.