Speaker
Description
Categorizing events using discriminant observables is central to many high-energy physics analyses. Yet, bin boundaries are often chosen manually. A simple, popular choice in multi-classification tasks is to assign events according to the largest per-class score (”argmax”) and to apply equidistant binning to the resulting one-dimensional discriminants. We propose a binning optimization for signal significance directly in multi-dimensional discriminants using a differentiable approach. We use a Gaussian Mixture Model (GMM) to define flexible regions in the score space, which can be interpreted either as bins or as analysis categories. While this GMM-based strategy is applicable in both one and multiple dimensions, we also study a direct bin-boundary optimization in one dimension as a simpler alternative for binary discriminants.
This talk presents the methods and results of the paper arXiv:2601.07756, with a particular focus on the differentiable optimization approach. We evaluate the method on binary and multi-class toy classification problems, as well as on the FAIR Universe $H\rightarrow\tau\tau$ benchmark dataset, demonstrating its applicability in realistic analysis scenarios. The proposed approach is compared with the equidistant binning strategy and with alternative binning methods based on k-means clustering and Bayesian optimization.