Model selection for amplitude analysis

TL;DR

This paper introduces a regularization-based method to optimize model complexity in amplitude analyses, improving predictive power and parameter resolution in multivariate Dalitz-plot studies.

Contribution

It proposes a data-driven regularization approach to select relevant amplitudes, enhancing model performance in amplitude analysis.

Findings

Regularization significantly improves model performance.

Method helps in determining the significance of resonances.

Reduces overfitting by controlling model complexity.

Abstract

Model complexity in amplitude analyses is often a priori under-constrained since the underlying theory permits a large number of possible amplitudes to contribute to most physical processes. The use of an overly complex model results in reduced predictive power and worse resolution on unknown parameters of interest. Therefore, it is common to reduce the complexity by removing from consideration some subset of the allowed amplitudes. This paper studies a method for limiting model complexity from the data sample itself through regularization during regression in the context of a multivariate (Dalitz-plot) analysis. The regularization technique applied greatly improves the performance. An outline of how to obtain the significance of a resonance in a multivariate amplitude analysis is also provided.