Optimizing observables with machine learning for better unfolding. (1st July 2022)

• Record Type: Journal Article
• Authors: Arratia, Miguel; Britzger, Daniel; Long, Owen; Nachman, Benjamin
• Is Part Of: Journal of instrumentation. Volume 17:Number 7(2022)
• Publication Date: 2022-07-01
• Subjects: Analysis and statistical methods -- Large detector-systems performance -- Performance of High Energy Physics Detectors -- Large detector systems for particle and astroparticle physics; Scientific apparatus and instruments -- Periodicals; 502.84
• DOI: 10.1088/1748-0221/17/07/P07009
• Languages: English
• ISSNs: 1748-0221

Abstract

Abstract: Most measurements in particle and nuclear physics use matrix-based unfolding algorithms to correct for detector effects. In nearly all cases, the observable is defined analogously at the particle and detector level. We point out that while the particle-level observable needs to be physically motivated to link with theory, the detector-level need not be and can be optimized. We show that using deep learning to define detector-level observables has the capability to improve the measurement when combined with standard unfolding methods.