Advancing set-conditional set generation: Diffusion models for fast simulation of reconstructed particles

Journal Article · 19 November 2024 · Physical Review. D.

DOI: https://doi.org/10.1103/PhysRevD.110.092013 · OSTI ID:2478042

Kobylianskii, Dmitrii ; Soybelman, Nathalie ; Kakati, Nilotpal ; Dreyer, Etienne ; Nachman, Benjamin ; Gross, Eilam

The computational intensity of detector simulation and event reconstruction poses a significant difficulty for data analysis in collider experiments. This challenge inspires the continued development of machine learning techniques to serve as efficient surrogate models. We propose a fast emulation approach that combines simulation and reconstruction. In other words, a neural network generates a set of reconstructed objects conditioned on input particle sets. To make this possible, we advance set-conditional set generation with diffusion models. Using a realistic, generic, and public detector simulation and reconstruction package (COCOA), we show how diffusion models can accurately model the complex spectrum of reconstructed particles inside jets. Published by the American Physical Society 2024

• Sponsoring Organization: USDOE
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 2478042
• Journal Information: Physical Review. D., Journal Name: Physical Review. D. Journal Issue: 9 Vol. 110; ISSN PRVDAQ; ISSN 2470-0010
• Publisher: American Physical SocietyCopyright Statement
• Country of Publication: United States
• Language: English