Speaker
Roger Huang
Description
It is well-known that classifiers can be trained to approximate the likelihood ratio between two distributions, and that machine learning based classifiers in particular can learn this likelihood ratio in high-dimensional spaces. This provides a method to reweight events from different distributions as functions of many features. OmniFold is an unfolding technique that uses this concept to perform unbinned, high-dimensional unfolding. This talk will present the application of OmniFold to a neutrino cross-section study using T2K public data and discuss potential future applications to other neutrino experiments.
