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数据展开成为可能，无需分箱且涵盖多维关联性。我们描述了一套基于机器学习的展开方法，包括已知的、改进的以及新提出的方法。这些方法的性能在相同的两组数据集上进行了评估。我们发现，所有技术都能在复杂可观测量上准确重现粒子级能谱。由于这些方法在概念上具有多样性，它们为一类新型测量提供了令人兴奋的工具箱，这类测量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。