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Conditional deep generative models for simultaneous simulation and reconstruction of entire events

Etienne Dreyer1, Eilam Gross1, Dmitrii Kobylianskii1,*, Vinicius Mikuni2, and Benjamin Nachman2,†

  • *Contact author: dmitry.kobylyansky@weizmann.ac.il
  • †Contact author: bpnachman@lbl.gov
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Phys. Rev. D 113, 032005 – Published 9 February, 2026

DOI: https://doi.org/10.1103/14ph-482n

Abstract

We extend the particle-flow neural assisted simulations (parnassus) framework of fast simulation and reconstruction to entire collider events. In particular, we use two generative artificial intelligence tools, continuous normalizing flows and diffusion models, to create a set of reconstructed particle-flow objects conditioned on truth-level particles from CMS Open Simulations. While previous work focused on jets, our updated methods now can accommodate all particle-flow objects in an event along with particle-level attributes like particle type and production vertex coordinates. This approach is fully automated, entirely written in Python, and GPU-compatible. Using a variety of physics processes at the LHC, we show that the extended parnassus is able to generalize beyond the training dataset and outperforms the standard, public tool delphes.

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Phys. Rev. D 113, 032005– Published 9 February, 2026
  • Received 15 April 2025
  • Accepted 23 October 2025
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