Speaker
Richard Tyson
(Jefferson Lab)
Description
The solution to many problems can be described by the ratio of the probability densities of two event samples. For example, detector acceptances can be modeled by the ratio of the probability density for detected (accepted) events over that for all events. Similarly, sWeights can be converted to positive definite probabilities via density ratio estimation in order to create machine learning training samples from experimental data. In this talk, I will describe how binary classification can be used towards density ratio estimation. I will then describe the two applications mentioned above in more detail and their potential use cases in hadron spectroscopy.
Author
Richard Tyson
(Jefferson Lab)
