Can I read Uncertainty Quantification for Wide-bin Unfolding: One-at-a-time Strict Bounds and Prior-optimized Confidence Intervals on EtoBox?

Uncertainty Quantification for Wide-bin Unfolding: One-at-a-time Strict Bounds and Prior-optimized Confidence Intervals by Stanley, Michael; Patil, Pratik; Kuusela, Mikael is a scholarly article available to read on EtoBox.

What is Uncertainty Quantification for Wide-bin Unfolding: One-at-a-time Strict Bounds and Prior-optimized Confidence Intervals about?

Unfolding is an ill-posed inverse problem in particle physics aiming to infer a true particle-level spectrum from smeared detector-level data. For computational and practical reasons, these spaces are typically discretized using histograms, and the smearing is modeled through a response matrix corresponding to a discretized smearing kernel of the particle detector. This response matrix depends on the unknown shape of the true spectrum, leading to a fundamental systematic uncertainty in the unfolding problem. To handle the ill-posed nature of the problem, common approaches regularize the problem either directly via methods such as Tikhonov regularization, or implicitly by using wide-bins in the true space that match the resolution of the detector. Unfortunately, both of these methods lead to a non-trivial bias in the unfolded estimator, thereby hampering frequentist coverage guarantees for confidence intervals constructed from these methods. We propose two new approaches to addressing the bias in the wide-bin setting through methods called One-at-a-time Strict Bounds (OSB) and Prior-Optimized (PO) intervals. The OSB intervals are a bin-wise modification of an existing guaranteed-cov

Author

Stanley, Michael; Patil, Pratik; Kuusela, Mikael

Published

2021

Language

EN