A data-driven convergence criterion for iterative unfolding of smeared spectra
arXiv:2101.01096
Abstract
A data-driven convergence criterion for the D'Agostini (Richardson-Lucy) iterative unfolding is presented. It relies on the unregularized spectrum (infinite number of iterations), and allows a safe estimation of the bias and undercoverage induced by truncating the algorithm. In addition, situations where the response matrix is not perfectly known are also discussed, and show that in most cases the unregularized spectrum is not an unbiased estimator of the true distribution. Whenever a bias is introduced, either by truncation of by poor knowledge of the response, a way to retrieve appropriate coverage properties is proposed.
arXiv comment17 pages, 9 figures
References in corpus (8)
- The GENIE Neutrino Monte Carlo Generator
- SVD Approach to Data Unfolding
- The NEUT Neutrino Interaction Simulation
- Improved iterative Bayesian unfolding
- Data Unfolding with Wiener-SVD Method
- Statistical unfolding of elementary particle spectra: Empirical Bayes estimation and bias-corrected uncertainty quantification
- Iterative unfolding with the Richardson-Lucy algorithm
- Analysis of distorted measurements -- parameter estimation and unfolding