July 31, 2017 to August 4, 2017
Fermi National Accelerator Laboratory
US/Central timezone

Data unfolding with Wiener-SVD Method

Aug 2, 2017, 11:55 AM
Hornets Nest (Fermi National Accelerator Laboratory)

Hornets Nest

Fermi National Accelerator Laboratory

Presentation Computing, Analysis Tools and Data Handling Computing, Analysis Tools, and Data Handling


Ms Xiaoyue Li (Stony Brook University)


Data unfolding is a commonly used technique in the HEP community, particularly in cross-section measurements. Inspired by the deconvolution technique in digital signal processing, we propose a new unfolding method based on Wiener filter and the SVD technique. Unlike traditional unfolding techniques, the Wiener-SVD unfolding method achieves data unfolding by maximizing signal to noise ratios in the effective frequency domain without having to scan over regularization strength. The mathematical formulation of the method and few applications of the Wiener-SVD unfolding technique will be presented; the advantages and disadvantages, as well as the nature of the unfolded results will be discussed.

Primary authors

Dr Wei Tang (BNL) Ms Xiaoyue Li (Stony Brook University)


Chao Zhang (Brookhaven National Laboratory)) Mr Hanyu Wei (Center for High Energy Physics, Tsinghua University) Dr Xin Qian (BNL)

Presentation materials