Speaker
Description
Measured distributions are usually distorted by a finite resolution of the detector. Within physics research, the necessary correction of these distortions is know as Unfolding. Machine learning research uses a different term for this very task: Quantification Learning. For the past two decades, this difference in terminology - together with several differences in notation - have prevented computer scientists and physicists from acknowledging the fact that Unfolding and Quantification Learning indeed cover the same mathematical problem.
In this talk, I will bridge the gap between these two branches of literature and I will provide an overview of the numerous key results that Quantification Learning has produced over the past two decades, covering statistical consistency, the anatomy of reconstruction errors, improved optimization techniques, more informative data representations, and arbitrary numbers of observable quantities. Each of these results has immediate and compelling implications on the practice of Unfolding, tackling questions like: Which algorithms produce trustworthy results and which ones don't? How can we increase their performance and how should we implement them? How much data do we need from which source? Which of the current limits in Unfolding are inherent and which can be lifted? We will discuss these questions from an interdisciplinary perspective, taking into account recent developments from both physics and machine learning research.
Significance
Current Unfolding techniques suffer from a series of limitations, such as maximum numbers of allowed input observables, inconsistent outputs, questionable optimization back-ends, and sub-optimal reconstruction quality. Several of these limitations have recently been overcome in the related field of Quantification Learning. This talk will elaborate on how to considerably improve Unfolding techniques with ideas from this related field. The improved techniques have immediate implications on any analysis that requires Unfolding, e.g., spectral reconstruction for Imaging Atmospheric Cherenkov Telescopes and differential cross section measurements at collider experiments.
References
https://doi.org/10.1007/s10618-024-01067-2
https://doi.org/10.18420/INF2022_37
https://doi.org/10.1007/978-3-031-43424-2_5
https://doi.org/10.1007/978-3-031-20467-8
