论文标题
使用深神经网络识别Hadronic Tau Lepton衰变
Identification of hadronic tau lepton decays using a deep neural network
论文作者
论文摘要
提出了一种新的算法，以区分tau leptons（$τ_\ Mathrm {h} $）的重建的HADRONIC衰变，该衰减源自CMS检测器中的真正Tau leptons，反对$τ_\ Mathrm {H Mathrm {H} $候选人，这些候选者来自Quark或Gluon Jets或Gluon Jets，electrons electrons electrons electrons electer或muons or muons or m uon。该算法输入来自$τ_\ Mathrm {H} $候选附近所有重建粒子的信息，并采用具有卷积层的深神经网络来有效地处理输入。与先前使用的算法相比，该算法可显着提高性能。例如，对于通过Quark和Gluon Jets的给定效率而言，真正的$τ_\ Mathrm {H} $的效率提高了10-30％。此外，引入了一种更有效的$τ_\ Mathrm {H} $重建，该重建结合了其他HADRONIC DECAY模式。新算法的出色性能以区分喷气机，电子和MUON以及改进的$τ_\ Mathrm {H} $重建方法的改进，用LHC Proton-Proton碰撞数据以$ \ sqrt {s} = $ 13 tev进行了验证。
A new algorithm is presented to discriminate reconstructed hadronic decays of tau leptons ($τ_\mathrm{h}$) that originate from genuine tau leptons in the CMS detector against $τ_\mathrm{h}$ candidates that originate from quark or gluon jets, electrons, or muons. The algorithm inputs information from all reconstructed particles in the vicinity of a $τ_\mathrm{h}$ candidate and employs a deep neural network with convolutional layers to efficiently process the inputs. This algorithm leads to a significantly improved performance compared with the previously used one. For example, the efficiency for a genuine $τ_\mathrm{h}$ to pass the discriminator against jets increases by 10-30% for a given efficiency for quark and gluon jets. Furthermore, a more efficient $τ_\mathrm{h}$ reconstruction is introduced that incorporates additional hadronic decay modes. The superior performance of the new algorithm to discriminate against jets, electrons, and muons and the improved $τ_\mathrm{h}$ reconstruction method are validated with LHC proton-proton collision data at $\sqrt{s} =$ 13 TeV.
