论文标题
将最近的邻居回归器和神经网络分类器结合的缺失共振的重建
Reconstruction of Missing Resonances Combining Nearest Neighbors Regressors and Neural Network Classifiers
论文作者
论文摘要
中微子，暗物质和长寿命的中性颗粒未被注意到粒子探测器，携带有关其母体颗粒和相互作用源的信息，以重建关键变量，例如不变质量分布中的共振峰。在这项工作中，我们表明，$ k $ - 最近的邻居回归算法与深神经网络分类器（$ k $ nn）结合使用，能够准确地从本新的重型higgs玻色子及其标准模型背景中从可观的探测器级别上恢复全部Leptonic $ ww $ ww $ ww $ ww $的分布。回归器的输出可用于训练更强大的分类器，以在完全缓慢的情况下分离信号和背景，并保证选择具有增强统计意义的质量壳希格斯玻色子。该方法假设事件类别和模型参数的先前知识，因此适用于分类后的研究。
Neutrinos, dark matter, and long-lived neutral particles traverse the particle detectors unnoticed, carrying away information about their parent particles and interaction sources needed to reconstruct key variables like resonance peaks in invariant mass distributions. In this work, we show that a $k$-nearest neighbors regressor algorithm combined with deep neural network classifiers, a $k$NN, is able to accurately recover binned distributions of the fully leptonic $WW$ mass of a new heavy Higgs boson and its Standard Model backgrounds from the observable detector level information at disposal. The output of the regressor can be used to train even stronger classifiers to separate signals and backgrounds in the fully leptonic case and guarantee the selection of on-mass-shell Higgs bosons with enhanced statistical significance. The method assumes previous knowledge of the event classes and model parameters, thus suitable for post-discovery studies.
