Refining Jets for CMS Run 3 using Fast Simulation

¹ Istanbul Technical University, Science and Letters Faculty, Istanbul, Turkiye
² Fermi National Accelerator Laboratory, Batavia, IL, USA
³ Université Catholique de Louvain, Louvain-la-Neuve, Belgium
⁴ University of Hamburg, Institute of Experimental Physics, Hamburg, Germany

^* e-mail: acelya.deniz.gungordu@cern.ch
^** e-mail: dorukhan.boncukcu@cern.ch
^*** e-mail: pedrok@cern.ch
^**** e-mail: samuel.bein@cern.ch

Published online: 7 October 2025

Abstract

As the LHC moves into its high-luminosity phase, the CMS experiment must handle more complex data collected at much higher rates. While the Geant4-based simulation application (FullSim) provides highly accurate simulation to complement real data, FullSim’s intensive consumption of computing resources becomes an increasing liability as the rates increase, while faster tools offer an advantage. The fast MC production application (FastSim) delivers a complete simulation with a factor of 10 speedup over FullSim, but introduces inaccuracies in some observables. A specialized refinement method, Fast Perfekt, employs machine learning to improve the accuracy of FastSim. An initial report of this work focused on the refinement of jet flavor tagging observables. This article presents an update on the refinement, focusing on PUPPI jets with Run 3 data-taking conditions. Refinement is extended to include jet transverse momentum as well as its propagation to missing transverse momentum. A gridbased framework and real-time monitoring system have been developed to facilitate optimization and scaling of the refinement to a large number of target variables.