An Iterative, Dynamically Stabilized(IDS) Method of Data Unfolding

TL;DR

This paper introduces an iterative data unfolding method that employs a regularization function to improve normalization, handle fluctuations, and reconstruct new structures in experimental data.

Contribution

It presents a novel iterative unfolding approach with dynamic stabilization and regularization, enhancing data analysis accuracy in experimental physics.

Findings

Effective in stabilizing data unfolding against fluctuations

Improves normalization of Monte Carlo spectra

Capable of reconstructing previously unmodeled structures

Abstract

We describe an iterative unfolding method for experimental data, making use of a regularization function. The use of this function allows one to build an improved normalization procedure for Monte Carlo spectra, unbiased by the presence of possible new structures in data. We unfold, in a dynamically stable way, data spectra which can be strongly affected by fluctuations in the background subtraction and simultaneously reconstruct structures which were not initially simulated.