论文标题
通过机器学习的可能性比进行共振搜索
Resonance Searches with Machine Learned Likelihood Ratios
论文作者
论文摘要
我们在具有重型Z'Boson的基准模型中演示了机器学习的似然比在共鸣搜索中的力量。似然比表示是多变量检测器级别可观察物的函数，但不是像基于矩阵元素的方法那样明确计算，而是从联合可能性比率中学到的，该联合可能性比率取决于模拟样本中的潜在信息。我们表明，使用机器学习的似然比绘制的边界比使用从直方图计算出的似然比绘制的可能性更高。
We demonstrate the power of machine-learned likelihood ratios for resonance searches in a benchmark model featuring a heavy Z' boson. The likelihood ratio is expressed as a function of multivariate detector level observables, but rather than being calculated explicitly as in matrix-element-based approaches, it is learned from a joint likelihood ratio which depends on latent information from simulated samples. We show that bounds drawn using the machine learned likelihood ratio are tighter than those drawn using a likelihood ratio calculated from histograms.
