Karlsruhe 2024 – scientific programme

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T: Fachverband Teilchenphysik

T 96: Data, AI, Computing 7 (uncertainties, likelihoods)

T 96.9: Talk

Thursday, March 7, 2024, 18:00–18:15, Geb. 30.33: MTI

Refining Fast Simulations using Machine Learning Techniques — Samuel Bein², Patrick Connor², Sebastian Götschel¹, Daniel Ruprecht¹, Peter Schleper², •Lars Stietz^1,2, and Moritz Wolf² — ¹Technische Universität Hamburg — ²Universität Hamburg

In the realm of particle physics, a large amount of data are produced in particle collision experiments such as the CERN Large Hadron Collider (LHC) to explore the subatomic structure of matter. Simulations of the particle collisions are needed to analyse the data recorded at the LHC. These simulations rely on Monte Carlo techniques to handle the high dimensionality of the data. Fast simulation methods (FastSim) have been developed to cope with the significant increase of data that will be produced in the coming years, providing simulated data 10 times faster than the conventional simulation methods (FullSim) at the cost of reduced accuracy. The currently achieved accuracy of FastSim prevents it from replacing FullSim. We propose a machine learning approach to refine high level observables reconstructed from FastSim with a regression network inspired from the ResNet approach. We combine the mean squared error (MSE) loss and the maximum mean discrepancy (MMD) loss. The MSE (MMD) compares pairs (ensembles) of data samples. We examine the strengths and weaknesses of each individual loss function and combine them as a Lagrangian optimization problem.

Keywords: Machine Learning; Refinement; FastSim; Regression; Residual Network