Moritz Wolf, Lars O. Stietz, Patrick L. S. Connor, Peter Schleper, Samuel Bein
SciPost Phys. Core 8, 021 (2025) ·
published 14 February 2025
|
· pdf
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.
SciPost Phys. Core 6, 040 (2023) ·
published 1 June 2023
|
· pdf
We motivate and describe a method based on fits with polynomials to test the smoothness of differential distributions. As a demonstration, we apply the method to several measurements of inclusive jet double-differential cross section in the jet transverse momentum and rapidity at the Tevatron and LHC. This method opens new possibilities to test the quality of differential distributions used for the extraction of physics quantities such as the strong coupling.
Dr Connor: "Dear referee, thank you ver..."
in Submissions | report on Step: a tool to perform tests of smoothness on differential distributions based on Chebyshev polynomials of the first kind