Monte Carlo simulation for the LHC experiments at CERN: A comprehensive overview

Monte Carlo (MC) simulations are essential for interpreting data from the Large Hadron Collider (LHC), connecting theoretical predictions with experimental measurements and providing corrections for detector acceptance and efficiency. This review presents an overview of MC simulation workflows in CERN’s major LHC experiments: ALICE, ATLAS, CMS, and LHCb. It details the multi-stage simulation pipeline, including event generation, detector simulation, digitization, and the experiment-specific software frameworks and computational infrastructures, such as the Worldwide LHC Computing Grid (WLCG). The article highlights computational challenges in high-precision simulations, adoption of Fast Simulation (FastSim) techniques, and optimizations via multithreading, GPU acceleration, and machine learning (ML). It also examines future scalability issues for the High-Luminosity LHC (HL-LHC), including generative models, hardware adaptation, and quantum computing approaches. This review provides both a technical reference and a forward-looking perspective for accurate, efficient, and scalable MC simulations in high energy physics research.