Maximizing Returns: Optimizing Experimental Observables at the LHC

TL;DR

This paper presents a new framework combining analytical and machine learning methods to optimize experimental observables at the LHC, improving data analysis efficiency and information retention.

Contribution

It introduces a novel metric for evaluating approaches and demonstrates how to effectively store and analyze data in fewer bins, reducing dimensionality and resource use.

Findings

Effective data compression into limited bins

Enhanced analysis efficiency and data preservation

Validated approach through Higgs boson process simulations

Abstract

We introduce a framework that integrates both analytical and machine-learning approaches for calculating observables optimal for EFT and broader applications at the LHC. A new metric for evaluating the performance of these approaches has been introduced. In addition, we demonstrate how the majority of relevant information can be effectively stored in a limited number of bins, allowing for efficient data analysis, data preservation, and global data combination, while also providing tools to achieve these benefits. A key feature of this approach is the reduction in the dimensionality of the 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 the Higgs boson production and decay processes at the LHC.