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Flow annealed importance sampling bootstrap meets differentiable particle physics

Kofler, Annalena [Max Planck Institute for Intelligent Systems, Tubingen (Germany); Max Planck Institute for Gravitational Physics, Potsdam (Germany); Technical Univ. of Munich (Germany)] (ORCID:0009000859386215)·Stimper, Vincent [Max Planck Institute for Intelligent Systems, Tubingen (Germany); Isomorphic Labs, London (United Kingdom); Univ. of Cambridge (United Kingdom)] (ORCID:0000000249654297)·Mikhasenko, Mikhail [Ruhr Univ., Bochum (Germany); Excellence Cluster ORIGINS, Garching (Germany)] (ORCID:0000000269692063)·Kagan, Michael [SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)] (ORCID:0000000233866869)·Heinrich, Lukas [Technical Univ. of Munich (Germany)] (ORCID:0000000240487584)
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Abstract

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.

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72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDSannealed importance samplingdifferentiable programminggenerative modelingnormalizing flowsparticle physics

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Kofler, Annalena [Max Planck Institute for Intelligent Systems, Tubingen (Germany); Max Planck Institute for Gravitational Physics, Potsdam (Germany); Technical Univ. of Munich (Germany)] (ORCID:0009000859386215), Stimper, Vincent [Max Planck Institute for Intelligent Systems, Tubingen (Germany); Isomorphic Labs, London (United Kingdom); Univ. of Cambridge (United Kingdom)] (ORCID:0000000249654297), Mikhasenko, Mikhail [Ruhr Univ., Bochum (Germany); Excellence Cluster ORIGINS, Garching (Germany)] (ORCID:0000000269692063), Kagan, Michael [SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)] (ORCID:0000000233866869), Heinrich, Lukas [Technical Univ. of Munich (Germany)] (ORCID:0000000240487584). 2025-06-12. Flow annealed importance sampling bootstrap meets differentiable particle physics. https://doi.org/10.1088/2632-2153%2Faddbc1

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