Fast perfekt: regression-based refinement of fast simulation

The availability of precise and accurate simulation is a limiting factor for interpreting and forecasting data in many fields of science and engineering. Often, one or more distinct simulation software applications are developed, each with a relative advantage in accuracy or speed. The quality of insights extracted from the data stands to increase if the accuracy of faster, more economical simulation could be improved to parity or near parity with more resource-intensive but accurate simulation. We present Fast Perfekt, a machine-learned regression to refine the output of fast simulation that employs residual neural networks. A deterministic morphing model is trained using a unique schedule that makes use of the ensemble loss function MMD, with the option of an additional pair-based loss function such as the MSE. We explore this methodology in the context of an abstract analytical model and in terms of a realistic particle physics application featuring jet properties in hadron collisions at the CERN Large Hadron Collider. The refinement makes maximum use of existing domain knowledge, and introduces minimal computational overhead to production.

DDC Class

006.3: Artificial Intelligence

530: Physics

539: Matter; Molecular Physics; Atomic and Nuclear physics; Radiation; Quantum Physics

004: Computer Sciences

Funding(s)

DASHH Helmholtz Graduiertenkolleg

Publication version

publishedVersion

Lizenz

https://creativecommons.org/licenses/by/4.0/

Name

SciPostPhysCore_8_1_021.pdf

Type

Main Article

Size

986.9 KB

Format

Adobe PDF

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Fast perfekt: regression-based refinement of fast simulation