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.

机器学习创新使得数据展开成为可能，而不需要分箱并且可以考虑多个维度之间的相关性。我们描述了一组已知的、升级的和新的基于机器学习的展开方法，并在相同的两个数据集上评估了这些方法的性能。我们发现所有技术都能够准确地再现复杂可观测量下的粒子水平谱。鉴于这些方法在概念上各不相同，它们为一类新的测量提供了令人兴奋的工具包，可以以前所未有的细节探索标准模型，并可能实现对新现象的敏感性。