Abstract
We present two methods for reducing multidimensional information to one dimension for ease of understand or analysis while maintaining statistical power. While not new, dimensional reduction is not greatly used in high-energy physics and has applications whenever there is a distinctive feature (for instance, a mass peak) in one variable but when signal purity depends on others; so in practice in most of the areas of physics analysis. While both methods presented here assume knowledge of the background, they differ in the fact that only one of the methods uses a model for the signal, trading some increase in statistical power for this model dependence.
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