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.
@article{arxiv.2405.10106,
title = {Advancing Set-Conditional Set Generation: Diffusion Models for Fast Simulation of Reconstructed Particles},
author = {Dmitrii Kobylianskii and Nathalie Soybelman and Nilotpal Kakati and Etienne Dreyer and Benjamin Nachman and Eilam Gross},
journal= {arXiv preprint arXiv:2405.10106},
year = {2024}
}