Speaker
Nitish Kasaraguppe
(RWTH Aachen (DE))
Description
Categorizing events using discriminant observables is central to many high-energy physics analyses. Yet, bin boundaries are often chosen by hand. A simple, popular choice is to apply argmax projections of multi-class scores and equidistant binning of one-dimensional discriminants.
This talk presents binning optimization for signal significance directly in multi-dimensional discriminants using a differentiable approach. We use a Gaussian Mixture Model to define flexible bin boundary shapes for multi-class scores, while in one dimension (binary classification), we move bin boundaries directly. The performance is evaluated on a toy binary classification example and on a three-class problem with two signal processes and one background.
Authors
Florian Mausolf
(RWTH Aachen (DE))
Johannes Erdmann
(RWTH Aachen University)
Nitish Kasaraguppe
(RWTH Aachen (DE))