Speaker
Description
Modern particle physics measurements increasingly rely on precise characterizations of subtle quantum effects, making the identification of optimal observables a significant challenge. We present a framework that defines a new metric for evaluating the performance of observables sensitive to quantum interference. We also demonstrate how most of the relevant multidimensional information to separate any given hypotheses can be effectively stored in a small number of bins, allowing for efficient data analysis, data preservation, and global data combination, while providing the tools to do so in the MiLoMerge package for Python. A key feature of this approach is the reduction in the dimensionality of observable information, which enhances both the effectiveness and practicality of the data analysis while maximizing gains within limited resources. These features have been demonstrated through simulated analyses of Higgs boson production and decay processes at the LHC.