CMS-PAS-MLG-24-001

CMS-PAS-MLG-24-001
Reweighting of simulated events using machine learning techniques in CMS
CMS Collaboration
19 July 2024

Abstract: Data analyses in particle physics rely on the accurate simulation of particle collisions and a detailed detector simulation to extract physical knowledge from the recorded data. Event generators together with a GEANT-based simulation of the detectors are used to produce large samples of simulated events for analysis by the LHC experiments. These simulations come at a high computational cost, where the detector simulation and reconstruction algorithms have the largest CPU demands. This note describes how machine learning (ML) techniques are used to reweight simulated samples to different model parameters or entirely different models. The ML method avoids the need for simulating the detector response multiple times by incorporating the relevant information in a single sample through event weights. Results are presented for reweightings to model variations and higher-order calculations in simulated top quark pair production at the LHC. This ML-based reweighting is an important element of the future computing model of the CMS Collaboration and will enable precision measurements with the CMS experiment at the High-Luminosity LHC.
Links: CDS record (PDF) ; Physics Briefing ; CADI line (restricted) ; These preliminary results are superseded in this paper, Submitted to CSBS. The superseded preliminary plots can be found here.

CMS Publications