CMS开放数据中基于CWoLa和TopicFlow的夸克-胶子标记
Quark-versus-gluon tagging in CMS Open Data with CWoLa and TopicFlow
AI总结：
利用CMS开放数据，比较弱监督（CWoLa、TopicFlow）与完全监督方法在夸克-胶子喷注标记中的表现，发现真实数据上排名反转，并验证鲁棒性。
摘要：
我们利用CMS开放数据研究弱监督学习在LHC上标记夸克和胶子喷注的性能。我们以$Z$+喷注和双喷注事件分别作为富夸克和富胶子的混合样本，并从2011年7 TeV采集的数据以及蒙特卡洛模拟中获取样本。CWoLa和TopicFlow模型在真实数据上训练，并与在模拟上训练的完全监督分类器进行比较。为了获得真实数据中区分能力的估计，我们考虑了三种不同的数据中夸克/胶子混合比例估计方法。与在模拟上评估模型时相比，我们发现完全监督和弱监督方法的排名发生反转。此外，基于数据的这些排名对测试集中混合比例的估计具有鲁棒性。最后，我们使用TopicFlow平滑小测试集中的统计涨落，并为真实数据中的性能提供不确定性。
英文摘要：
We use the CMS Open Data to examine the performance of weakly-supervised learning for tagging quark and gluon jets at the LHC. We target $Z$+jet and dijet events as respective quark- and gluon-enriched mixtures and derive samples both from data taken in 2011 at 7 TeV, and from Monte Carlo. CWoLa and TopicFlow models are trained on real data and compared to fully-supervised classifiers trained on simulation. In order to obtain estimates for the discrimination power in real data, we consider three different estimates of the quark/gluon mixture fractions in the data. Compared to when the models are evaluated on simulation, we find reversed rankings for the fully- and weakly-supervised approaches. Further, these rankings based on data are robust to the estimate of the mixture fraction in the test set. Finally, we use TopicFlow to smooth statistical fluctuations in the small testing set, and to provide uncertainty on the performance in real data.
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