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Event Generation and Density Estimation with Surjective Normalizing Flows
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Normalizing flows are a class of generative models that enable exact likelihood evaluation. While these models have already found various applications in particle physics, normalizing flows are not flexible enough to model many of the peripheral features of collision events. Using the framework of Nielsen et al. (2020), we introduce several surjective and stochastic transform layers to a baseline normalizing flow to improve modelling of permutation symmetry, varying dimensionality and discrete features, which are all commonly encountered in particle physics events. We assess their efficacy in the context of the generation of a matrix element-level process, and in the context of anomaly detection in detector-level LHC events.
Forward citations
Cited by 4 Pith papers
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Schr\"{o}dinger Generator for High-Dimensional Integration and Sampling on Quantum Many-Body States
A two-stage sampler (adaptive marginal map plus normalizing flow, then resampling) is proposed and shown on model nuclear densities up to D=624, though a core Jacobian equation appears sign-inconsistent.
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A universal vision transformer for fast calorimeter simulations
A vision-transformer flow-matching model generates calorimeter showers across regular and irregular detector geometries at millisecond speeds, and pretraining plus fine-tuning cuts training cost by about half.
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Efficient many-jet event generation with Flow Matching
A Continuous Normalizing Flow trained with Flow Matching improves unweighting efficiency in high-multiplicity Drell-Yan and top-pair event generation by factors of 150 and 17 over Vegas.
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Mono-Z Dark Matter Search with Neural Spline Flows Using CMS Run 2015D Open Data
A mono-Z dark matter search with neural spline flows on CMS 2015 open data sets limits of mu<0.018–0.050, but these limits are invalid because the background model fails in the high-MET tail.
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