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.

翻译：机器学习的最新创新使得无需分箱且包含多维相关性的数据解卷积成为可能。我们描述了一系列已知、改进和基于机器学习的解卷积新方法。这些方法的性能在两个相同数据集上进行了评估。我们发现所有技术均能准确再现复杂可观测量上的粒子级谱。鉴于这些方法在概念上具有多样性，它们为新型测量提供了一个令人兴奋的工具箱，能够以前所未有的细节水平探测标准模型，并可能增强对新现象的灵敏度。