HDSense: An efficient method for ranking observable sensitivity

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.

💡 Research Summary

The paper introduces HDSense (High‑Dimensional Sensitivity), a fast, scalable metric for ranking sets of observables when full likelihoods—including all inter‑observable correlations—are unavailable or too costly to evaluate. The authors start from the Fisher information formalism, noting that the full Fisher matrix requires the joint probability density of all observables. In many practical problems, especially in particle‑physics hadronization tuning, only one‑dimensional marginal distributions (histograms) are readily available.

For each observable (O_i) the authors compute a single‑observable Fisher matrix
(I^{(i)}{ab}=E{p_i}!\left