sFit：一种极大似然拟合中的背景扣除方法
sFit: a method for background subtraction in maximum likelihood fit
摘要：
本文提出了一种在极大似然拟合中扣除背景的统计方法，而不依赖于任何单独的边带或模拟来进行背景建模。该方法称为sFit，是对sPlot技术的扩展，最初开发该技术是为了重建每个日期组件的真实分布。为sPlot技术定义的sWeights允许仅使用信号概率密度函数和信号区域中的事件来构造修改的似然函数。信号区域中的背景事件对似然函数的贡献在统计基础上抵消。最大化该似然函数导致信号概率密度函数中的拟合参数的无偏估计。
英文摘要：
This paper presents a statistical method to subtract background in maximum likelihood fit, without relying on any separate sideband or simulation for background modeling. The method, called sFit, is an extension to the sPlot technique originally developed to reconstruct true distribution for each date component. The sWeights defined for the sPlot technique allow to construct a modified likelihood function using only the signal probability density function and events in the signal region. Contribution of background events in the signal region to the likelihood function cancels out on a statistical basis. Maximizing this likelihood function leads to unbiased estimates of the fit parameters in the signal probability density function.
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