# Preserving physically important variables in optimal event selections: A case study in Higgs physics

## TL;DR

This paper introduces a novel differentiable mutual information-based method to create event discriminants that preserve important physical variable distributions in collider data analysis, enhancing Higgs boson signal extraction.

## Contribution

It presents a new decorrelation technique using mutual information estimates, improving physical distribution preservation in machine learning-based event selection.

## Key findings

- Achieves state-of-the-art decorrelation performance.
- Improves Higgs signal sensitivity compared to previous methods.
- Maintains physical variable distributions effectively.

## Abstract

Analyses of collider data, often assisted by modern Machine Learning methods, condense a number of observables into a few powerful discriminants for the separation of the targeted signal process from the contributing backgrounds. These discriminants are highly correlated with important physical observables; using them in the event selection thus leads to the distortion of physically relevant distributions. We present a novel method based on a differentiable estimate of mutual information, a measure of non-linear dependency between variables, to construct a discriminant that is statistically independent of a number of selected observables, and so manages to preserve their distributions in the event selection. Our strategy is evaluated in a realistic setting, the analysis of the Standard Model Higgs boson decaying into a pair of bottom quarks. Using the distribution of the invariant mass of the di-b-jet system to extract the Higgs boson signal strength, our method achieves state-of-the-art performance compared to other decorrelation techniques, while significantly improving the sensitivity of a similar, cut-based, analysis published by ATLAS.