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High Energy Physics - Phenomenology
Title: The Landscape of Unfolding with Machine Learning
(Submitted on 29 Apr 2024 (v1), last revised 17 May 2024 (this version, v2))
Abstract: Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions. We describe a set of known, upgraded, and new methods for ML-based unfolding. The performance of these approaches are evaluated on the same two datasets. We find that all techniques are capable of accurately reproducing the particle-level spectra across complex observables. Given that these approaches are conceptually diverse, they offer an exciting toolkit for a new class of measurements that can probe the Standard Model with an unprecedented level of detail and may enable sensitivity to new phenomena.
Submission history
From: Nathan Huetsch [view email][v1] Mon, 29 Apr 2024 15:44:35 GMT (7328kb,D)
[v2] Fri, 17 May 2024 07:13:07 GMT (7085kb,D)
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