OmniFold: A Method to Simultaneously Unfold All Observables
OmniFold: A Method to Simultaneously Unfold All Observables
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DOI：
10.1103/physrevlett.124.182001
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发表时间：
2020-05-07
影响因子：
8.6
通讯作者：
Thaler, Jesse
中科院分区：
文献类型：
--
作者：
Andreassen, Anders;Komiske, Patrick T.;Thaler, Jesse
对撞机的数据必须根据探测器效应（“未展开”）进行校正，以便与许多理论计算和其他实验的测量结果进行比较。传统上，展开是针对单独的、分箱的可观测量进行的，而不包括与表征检测器响应相关的所有信息。我们介绍了OMNIFOLD，一种展开方法，它使用机器学习来利用所有可用的信息，迭代地对模拟数据集进行重新加权。我们的方法是unbinned，适用于任意高维数据，并自然地结合了来自全相空间的信息。我们说明了这种技术的一个现实的射流子结构的例子，从大型强子对撞机，并比较它的标准分仓展开方法。这种新的范式能够同时测量所有的可观测量，包括那些在分析时尚未发明的。
Collider data must be corrected for detector effects ("unfolded") to be compared with many theoretical calculations and measurements from other experiments. Unfolding is traditionally done for individual, binned observables without including all information relevant for characterizing the detector response. We introduce OMNIFOLD, an unfolding method that iteratively reweights a simulated dataset, using machine learning to capitalize on all available information. Our approach is unbinned, works for arbitrarily high-dimensional data, and naturally incorporates information from the full phase space. We illustrate this technique on a realistic jet substructure example from the Large Hadron Collider and compare it to standard binned unfolding methods. This new paradigm enables the simultaneous measurement of all observables, including those not yet invented at the time of the analysis.