# Data Unfolding with Wiener-SVD Method

## TL;DR

This paper introduces a novel data unfolding method in high-energy physics that combines SVD and Wiener filtering to improve signal extraction without needing regularization parameters.

## Contribution

The Wiener-SVD unfolding technique is a new approach that maximizes signal-to-noise ratio in the frequency domain, inspired by digital signal processing methods.

## Key findings

- Effective in maximizing signal-to-noise ratio
- Free from regularization parameter
- Discusses advantages and limitations

## Abstract

Data unfolding is a common analysis technique used in HEP data analysis. Inspired by the deconvolution technique in the digital signal processing, a new unfolding technique based on the SVD technique and the well-known Wiener filter is introduced. The Wiener-SVD unfolding approach achieves the unfolding by maximizing the signal to noise ratios in the effective frequency domain given expectations of signal and noise and is free from regularization parameter. Through a couple examples, the pros and cons of the Wiener-SVD approach as well as the nature of the unfolded results are discussed.