20.500.12556/RUL-168209 Systematic evaluation of generative machine learning capability to simulate distributions of observables at the large hadron collider Monte Carlo simulations are a crucial component when analysing the Standard Model and New physics processes at the Large Hadron Collider. This paper aims to explore the performance of generative models for complementing the statistics of classical Monte Carlo simulations in the final stage of data analysis by generating additional synthetic data that follows the same kinematic distributions for a limited set of analysis-specific observables to a high precision. Several deep generative models are adapted for this task and their performance is systematically evaluated using a well-known benchmark sample containing the Higgs boson production beyond the Standard Model and the corresponding irreducible background. The paper evaluates the autoregressive models and normalizing flows and the applicability of these models using different model configurations is investigated. The best performing model is chosen for a further evaluation using a set of statistical procedures and a simplified physics analysis. By implementing and performing a series of statistical tests and evaluations we show that a machine-learning-based generative procedure can be used to generate synthetic data that matches the original samples closely enough and that it can therefore be incorporated in the final stage of a physics analysis with some given systematic uncertainty. physics of elementary particles experimental physics fizika osnovnih delcev eksperimentalna fizika true false true Angleški jezik Slovenski jezik Članek v reviji 2025-04-02 15:40:33 2025-04-02 15:40:38 2025-04-03 04:02:37 0000-00-00 00:00:00 2024 0 0 31 str. art. no. ǂ911 Vol. 84 Sep. 2024 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 539.12 1434-6052 10.1140/epjc/s10052-024-13284-6 231206915 s10052-024-13284-6.pdf s10052-024-13284-6.pdf 1 5406934C76C61EA2C731B880D83B3C85 2c40a491a98b962cc679415128f80c01ddedabc9b76281a52f02c5be1e9b3c32 5c7df2bd-0fc7-11f0-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=201322 https://link.springer.com/article/10.1140/epjc/s10052-024-13284-6 0 6536e4fd-0fc7-11f0-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=201323 Fakulteta za matematiko in fiziko 0 0 0