Horizontal Morphing Demo
Sven Kreiss, Kyle Cranmer
June 2013
Drag the sliders.
Small KD-trees
When walking the tree, it is easy to keep track of the volume of the rectangles. The opacity of the rectangles is proportional to the inverse of the volume. As it turns out, this might be a poor approximation as a probability density, so something else like KEYS needs to be layered on top.
\(m_H\):
Two Gaussians
These are 2x 200 points drawn from two Gaussians. For small mH, the Gaussian is on the left and narrow along m34 / MEKD, and for larger mH, the Gaussian is on the right and is wider in m34 / MEKD.
\(m_H\):
Four Gaussians, Two Parameters
These are 4x 200 points drawn from four Gaussians, two narrow and two broad. This is supposed to mimic the situation of shifting shapes and event-by-event errors.
\(m_H\):
\(m_{4l,err}\):