HDSense: An efficient method for ranking observable sensitivity

TL;DR

HDSense is a computationally efficient metric for ranking the constraining power of observable sets in high-dimensional parameter estimation, especially useful when correlations are unknown or hard to model.

Contribution

The paper introduces HDSense, a novel score that profiles over unknown correlations to efficiently rank observables using only one-dimensional histograms.

Findings

HDSense accurately identifies near-optimal observable subsets.

Validation shows HDSense matches full-likelihood methods in ranking effectiveness.

Framework handles data from multiple experiments with detector effects.

Abstract

Identifying which observables most effectively constrain model parameters can be computationally prohibitive when considering full likelihoods of many correlated observables. This is especially important for, e.g., hadronization models, where high precision is required to interpret the results of collider experiments. We introduce the High-Dimensional Sensitivity (HDSense) score, a computationally efficient metric for ranking observable sets using only one-dimensional histograms. Derived by profiling over unknown correlations in the Fisher information framework, the score balances total information content against redundancy between observables. We apply HDSense to rank a set observables in terms of their constraining power with respect to five parameters of the Lund string model of hadronization implemented in Pythia using simulated leptonic collider events at the $Z$ pole. Validation against machine-learning--based full-likelihood approximations demonstrates that HDSense successfully identifies near-optimal observable subsets. The framework naturally handles data from multiple experiments with different acceptances and incorporates detector effects. While demonstrated on hadronization models, the methodology applies broadly to generic parameter estimation problems where correlations are unknown or difficult to model.