论文标题
部分可观测时空混沌系统的无模型预测
Fatjet signatures of heavy neutrinos and heavy leptons in a left-right model with universal seesaw at the HL-LHC
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
We perform a collider search for fatjet signals originating from boosted heavy neutral and charged leptons with masses between a few hundred GeV to a TeV. These heavy leptons originate from the decay of heavy gauge bosons with masses above 4 TeV in a left-right symmetric extension of the Standard Model (SM), which considers a universal seesaw mechanism for the generation of all the SM fermion masses. The fatjet signals arise naturally in this model due to the presence of heavy seesaw partners of the SM fermions which decay to SM gauge bosons carrying large boosts. We employ substructure based variables lepton sub-jet fraction ($LSF$) and lepton mass drop ($LMD$) together with kinematic variables of fatjets to look for fatjet signals associated with non-isolated leptons. These variables help in reducing the SM backgrounds while retaining enough statistics for signal events, which leads to a robust discovery potential at the high-luminosity Large Hadron Collider (HL-LHC).
