无分箱展开工具
Tools for Unbinned Unfolding
AI总结：
本文开发了无分箱展开的方法、基准和软件，利用提升决策树和深度学习模型，并扩展数据集以包含接受效应，同时提供基于RooUnfold和PyPI的OmniFold软件包，以促进标准化应用。
摘要：
机器学习已使得微分截面测量不再需要离散化。这些无分箱展开方法超越了传统的基于直方图的范式，正迅速被整合到实验工作流程中。为了促进广泛的采用和标准化，我们开发了用于无分箱展开的方法、基准和软件。在方法论方面，我们展示了提升决策树在具有相对少量高维特征的展开中的效用，这补充了能够展开全相空间的先进深度学习模型。为了对无分箱展开方法进行基准测试，我们扩展了现有数据集以包含接受效应，这是实际测量中必要的挑战。此外，我们直接比较了分箱和无分箱方法，对后者使用离散化输入以控制分箱本身的影响。最后，我们为OmniFold无分箱展开方法组装了两个软件包，这些软件包应作为未来使用该技术进行任何分析的起点。其中一个软件包基于广泛使用的RooUnfold框架，另一个是通过Python包索引（PyPI）提供的独立软件包。
英文摘要：
Machine learning has enabled differential cross section measurements that are not discretized. Going beyond the traditional histogram-based paradigm, these unbinned unfolding methods are rapidly being integrated into experimental workflows. In order to enable widespread adaptation and standardization, we develop methods, benchmarks, and software for unbinned unfolding. For methodology, we demonstrate the utility of boosted decision trees for unfolding with a relatively small number of high-level features. This complements state-of-the-art deep learning models capable of unfolding the full phase space. To benchmark unbinned unfolding methods, we develop an extension of existing dataset to include acceptance effects, a necessary challenge for real measurements. Additionally, we directly compare binned and unbinned methods using discretized inputs for the latter in order to control for the binning itself. Lastly, we have assembled two software packages for the OmniFold unbinned unfolding method that should serve as the starting point for any future analyses using this technique. One package is based on the widely-used RooUnfold framework and the other is a standalone package available through the Python Package Index (PyPI).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
